Re(AI)magining
Client Success
For enterprises, the defining challenge of AI is no longer imagination. It is execution. Pilots have shown what is possible. The next test is harder: scaling AI inside the systems, workflows and regulatory environments where the business actually runs.
At enterprise scale, value is not created by intelligence alone. It is created when intelligence is engineered into the operating fabric of the organisation.
Across banking, financial services, insurance, healthcare, life sciences and technology, our clients are moving from AI experimentation to AI-enabled performance. Pricing changes that once took six weeks now happen in minutes. Claims that once took days to review become decision-ready in minutes. Workflows that depended on manual effort now move with greater speed, consistency and confidence.
These outcomes did not come from a model performing well in isolation. They came from pairing AI ambition with engineering discipline, trusted data, domain context and the operating architecture required to carry an idea from first proof into production.
The stories that follow show how AI becomes enterprise value when it is engineered for scale, trust and measurable impact. They are about durable outcomes our clients can build on, and about what becomes possible when intelligence is embedded into the way an enterprise actually works.
This is what Re(AI)magining™ Client Success means at Persistent.
Learn more about our client successes
https://www.persistent.com/client-success/
BFSI
COMMUNICATION, MEDIA & TELECOM
CONSUMER TECH
SOFTWARE & HI-TECH
HEALTHCARE
A leading cybersecurity software company offers SaaS products across data loss prevention, identity and cloud security, compliance and governance. Its products protect enterprise customers with stringent security and compliance needs. Managing the security of its own applications and infrastructure to the same standard it sets for customers was non-negotiable.
The challenge was scale and fragmentation. A large vulnerability backlog had accumulated across applications and infrastructure, spanning OS, container and code-level exposures. Visibility across tools including Tenable, Wiz and Snyk was inconsistent. Ownership was distributed across more than 60 teams, leading to varied remediation practices and patching gaps. Tracking residual vulnerabilities after remediation and maintaining audit-ready reporting added further complexity.
Persistent implemented a holistic vulnerability management and patching transformation, combining process standardisation, automation and GenAI-led intelligence across the full remediation lifecycle.
Reactive vulnerability management became a proactive, governed practice.
GenAI-assisted analysis helped interpret vulnerability descriptions, CVEs and security advisories to summarise risk impact and recommend prioritisation beyond standard CVSS scores, factoring in exploitability and exposure. Automated remediation guidance was generated at the code and configuration level, covering dependency upgrade paths, patch instructions and configuration fixes. This accelerated resolution for recurring vulnerabilities across products and reduced reliance on manual analysis at every step.
AI-driven reporting gave leadership real-time visibility into risk posture, closure trends and residual risks, with executive summaries generated automatically from vulnerability data. A centralised knowledge base and GenAI-generated remediation playbooks standardised fixes for recurring CVEs and product-specific patterns, improving onboarding speed and operational consistency across all 60-plus teams.
Unified visibility replaced fragmented ownership across the estate.
The results were significant. Critical and high vulnerabilities fell by 77%. Vulnerability triage and remediation ran 38% faster using GenAI. Closure velocity improved by 22% through standardised processes, freeing engineering teams to focus on higher-value work. Single-pane visibility across assets and vulnerabilities improved audit readiness, with full traceability and reporting embedded into the operating model.
The engagement established a business-as-usual vulnerability management practice with a defined governance model spanning application, security and infrastructure teams. Institutionalised reporting, audits and exception handling gave the client a repeatable, scalable foundation for managing security posture as its product portfolio and infrastructure continue to grow.
From Vulnerability Backlog to Security Posture Strength
The transformation brought intelligence, governance and consistency to the full remediation lifecycle.
77%
Reduction in critical and high vulnerabilities across applications and infrastructure
38%
Faster vulnerability triage and remediation using GenAI
22%
Improved closure velocity through standardised processes
60+ teams
Unified under a single governed vulnerability management practice
Knowledge Transfer, Engineered for Speed
SASVA™
Accelerated pre-transition assessment, reducing the overall risk profile of the handover
25%
Reduction in Knowledge Transition duration
30%
Reduction in shadow and handholding phase
Risk-first
Security vulnerabilities identified and mitigation strategy in place before Day 1
Ongoing
Transition established foundation for long-term managed partnership
A global leader in sales enablement technology serves more than 2,000 enterprise customers, including several Fortune 500 companies, helping go-to-market teams deliver personalised, data-driven buyer experiences through content automation, analytics, learning and seller productivity tools.
As the sales enablement market evolved rapidly, the company needed to strengthen its competitive edge on three fronts: accelerate innovation, expand platform adoption and reduce the cost and complexity of delivery. Persistent was engaged as a strategic product engineering partner to help modernise the platform and embed GenAI across engineering and product workflows.
The engagement unfolded across three phases. First came stabilisation: Persistent mobilised a 100-member India-based product engineering team, embedded GenAI tools into quality engineering and improved release velocity. Then came modernisation: legacy applications were consolidated, core technology components were upgraded, cloud infrastructure was optimised and monolithic services were re-architected to improve scalability and performance. Finally, came innovation: AI was embedded into workflows, agent-friendly APIs were developed and new customer-facing capabilities were delivered at pace.
Velocity became the measure of competitive advantage.
Persistent integrated GitHub Copilot and GenAI accelerators across the software development lifecycle, helping drive a 25% productivity uplift across developer and seller workflows. GenAIenabled testing increased automation coverage from 15% to 48%, with 80% of critical test cases automated. This contributed to a 60% reduction in severe post-production defects, improving release quality and strengthening customer confidence.
Platform modernisation ran in parallel. Persistent simplified the client's legacy technology landscape, optimised cloud infrastructure and improved platform scalability and performance. An in-house AIdriven reporting suite replaced third-party software, delivering more than $1 million in annual cost savings.
The transformation also strengthened the seller experience. Deeper integrations with collaboration, CRM, meeting and cloud storage platforms helped create a more unified workspace. Configurable GenAI prompts enabled more intelligent, contextual search, while new AI capabilities laid the foundation for more adaptive and agentic workflows.
Across teams adopting GenAI for learning, experimentation, development and execution, task-level productivity gains reached 30–40%. What began as a modernisation programme became a growth engine, helping the client improve speed, quality, cost efficiency and AI readiness at enterprise scale.
Rebuilding a Sales Enablement Leader for the Generative AI Era
A modernisation programme became a growth engine.
$1M+
Annual cost savings from replacing third-party reporting software
25%
Productivity uplift through GenAI adoption across the SDLC
80%
Critical test cases automated using GenAI-enabled QA
60%
Reduction in severe post-production customer defects
30-40%
Task-level productivity gains across teams using GenAI
Governed
Vulnerability management embedded with audits, exception handling and defined ownership
A leading global technology company develops and markets a broad portfolio of data, automation, security and infrastructure products. Among its most strategic investments is a suite of GenAI products designed to accelerate how enterprises build, develop and operate software. Deploying those same capabilities across its own engineering teams was both a natural extension of the strategy and a real-world test of enterprise-scale value.
The need became urgent following a critical customer incident. Excessive memory utilisation caused system outages that disrupted a billing platform, with significant downstream business impact. Diagnosing the root cause required deep investigation into a 25-year-old database engine, where legacy memory management complexity extended the time required for diagnosis and resolution. Resolving the issue faster and strengthening the team's ability to handle similar incidents in the future required a different approach.
The same GenAI capabilities built for customers had to deliver for the engineering teams building them.
Persistent took over the client's own GenAI product across the software development lifecycle, from requirements documentation through to pullrequest review. For the billing platform incident, the team used the GenAI platform to diagnose a complex memory deallocation issue buried within multi-layered exception blocks, a class of problem that would typically require extended specialist investigation. The combination of deep product knowledge and the platform's data processing capabilities accelerated both diagnosis and resolution.
The approach extended well beyond the incident. GenAI was applied across product development activities, supporting requirement generation, code development, testing and review. Teams built familiarity and confidence with the tools, improving both speed and quality across routine engineering work as well as complex legacy problem-solving.
Productivity gains compounded as GenAI was embedded deeper into the engineering workflow.
The results validated the investment. Productivity improved by approximately 20% across the software development lifecycle. Time to market for critical escalations improved by 30 to 50%. The billing platform incident that had previously required an extended investigation was resolved significantly faster. Across the team, confidence grew, both in the GenAI tooling and in the team's ability to tackle complex legacy issues with greater speed and precision.
The engagement demonstrated something the client could take back to its own customers: GenAI delivers measurable engineering value not just in greenfield development, but in the most complex corners of legacy infrastructure.
GenAI delivered measurable value across new development and the hardest legacy problems.
~20%
Productivity improvement across the software development lifecycle
30–50%
Improvement in time to market for critical escalations
25+ years
Legacy database engine complexity navigated using GenAI-assisted diagnosis
Full SDLC
GenAI embedded across every stage of the software development lifecycle
Engineering Velocity at Scale Through Generative AI Adoption
For a leading global online home goods retailer, the product catalogue is the digital storefront. Every product attribute — colour, material, dimension, finish — influences how customers search, compare, trust and buy. At a scale of approximately 30 million items across nearly 1,000 product classes, catalogue quality is not an operational detail. It is a growth lever.
The retailer engaged Persistent to modernise how product content is created, enriched and maintained across its ecosystem. The objective was to move beyond manual corrections and bespoke models towards a reusable intelligence layer embedded directly into catalogue and supplier operations.
The constraint was never AI capability. It was 47,000 attributes and one architecture to serve them all.
Catalogue quality depended heavily on suppliers and customers identifying inaccuracies after they occurred. Early AI models built for individual tags worked technically, but were expensive to build, difficult to maintain and not viable across roughly 47,000 attributes. The larger constraint was the human effort required to define, interpret and route every attribute and supplier issue accurately.
Persistent designed and delivered a Product Content Intelligence programme that replaced fragmented manual workflows with a modular, schema-driven enrichment platform built for production use.
At the core of the solution was a tag-agnostic enrichment engine connecting two reusable services. The Product Enricher orchestrates product data across the ecosystem and applies contextual meaning to each attribute. Tagging-as-a-Service provides a production-grade inference and storage pipeline that classifies attributes consistently across product classes.
To industrialise GenAI access, Persistent also built an LLM Gateway — a governed, scalable entry point that gives enrichment workloads reliable access to large language models. Trust was engineered into the workflow. High-confidence updates are applied automatically and suppliers are notified. Lowerconfidence or high-risk changes are routed for supplier confirmation before publication.
The result is a cloud-native, horizontally scalable platform built on GoLang, Python, Google BigQuery, GraphQL and Kubernetes — and designed to grow with the catalogue it serves.
The impact was not incremental. It was structural.
More than 2.5 million product tags were corrected across more than one million of the retailer's most visible and most-purchased items. More than one million products are now enriched in production, with new attribute coverage expanding approximately 70x year-over-year. Supplier support was also transformed, with 41,000 tickets automated each month and up to 70% automation achieved in select workflows. The first agentic triage workflows moved from prototype to production in approximately 2 months.
Rewiring Catalogue Intelligence for a 30-Million-Product Storefront
A catalogue that once depended on people noticing what was wrong now improves itself — at the scale of millions.
2.5M+
Product tags corrected across the most visible, most-purchased items
1M+
Products enriched in production; attribute coverage expanding ~70x year-over-year
41K
Supplier tickets automated per month, with up to 70% automation in select workflows
~2 months
Prototype to production for first agentic triage workflows
A global technology leader operating at significant data centre scale depends on rigorous engineering governance to keep its infrastructure expansion on track. Every equipment design submittal has to be validated against defined industry standards before it can move into approval and manufacturing. As the client's infrastructure footprint expanded, the volume and complexity of design submittals grew faster than the existing review model could comfortably handle.
The friction showed up in four ways. Engineering teams spent significant time comparing technical drawings and specifications against reference standards line by line. Because validation depended heavily on human interpretation, evaluation outcomes could vary from one reviewer to another. Review delays slowed the next stage in the lifecycle, extending turnaround times across the data centre ecosystem. And as submission volumes grew, dependence on a limited pool of specialist engineers made higher throughput harder to sustain.
Consistent, explainable review at scale became the priority.
Persistent built a GenAI-powered engineering design evaluation solution using its proprietary DesignEvalArmor accelerator. The platform analyses design submittals, maps them to applicable industry standards including IEEE and generates explainable outputs that support faster engineering review. Engineers upload the design submittal along with the relevant guideline documents, after which the platform evaluates compliance, flags deviations, recommends remediation actions and produces decision-ready outputs for sign-off.
Four design choices shaped the solution. DesignEvalArmor provided the core foundation, enabling faster time to value through a reusable framework purpose-built for engineering design evaluation. The solution interprets engineering drawings and design documents using similarity scoring, contextual reasoning and standards-aware evaluation. Rather than producing generic summaries, the platform highlights deviations, explains nonalignment and recommends remediation actions engineers can take back to vendors. A built-in SME feedback loop allows engineers to validate outputs, correct them where needed and continuously improve the system over time.
Expert review, made scalable.
The results were measurable. Engineering design analysis and evaluations ran twice as fast. Manual review effort dropped by approximately 30%. Automated compliance coverage against defined industry standards reached 85 to 90%. Turnaround times for design submittal approvals were reduced, and evaluations became more consistent across teams. Explainable outputs improved trust in the process and helped SME expertise go further.
The engagement established a foundation for broader transformation across the client's data centre engineering landscape. The next phase is focused on expanding AI-assisted evaluation into additional engineering review use cases, building a reusable validation framework across future submittal types and advancing the client's broader AI transformation agenda through domain-specific, explainable AI.
Making Engineering Design Reviews Faster, More Consistent and Explainable
When consistency improves upstream, the whole approval cycle accelerates.
Faster engineering design analysis and evaluations
~30%
Reduction in manual review effort
85–90%
Automated compliance coverage against defined industry standards
Scalable
Reusable validation framework expanding across engineering review use cases
An analyst at a global leader in mission-grade risk intelligence could spend days on a single investigation and still not be confident the most critical signal had surfaced.
Their proprietary intelligence platform aggregates, processes and surfaces signals from billions of open-source, deep web and dark web records across more than two hundred languages, in real time, for analysts operating in some of the world's most demanding investigative environments across government, defence and regulated sectors.
Billions of records across two hundred languages, streaming continuously from open sources, the deep web and the dark web. The data was there. But finding what mattered, in time to act on it, required manually searching, reconciling and interpreting across systems that had never been designed to work together. Meanwhile, the threat landscape was evolving faster than investigative methods could track — synthetic identities, coordinated disinformation, adversarial signals deliberately engineered to look like noise. The harder analysts worked, the wider the gap grew.
The bottleneck was not intelligence. It was the infrastructure built to deliver it.
Persistent took end-to-end managed ownership — not just of the technology build, but of the recurring collection, repair and new spider development that kept the intelligence pipeline running. This shared-accountability model meant outcomes were Persistent’s responsibility, not just delivery milestones. Using its production-grade agentic framework — a pre-assembled, pre-integrated harness that teams step into and start delivering with immediately — fragmented manual workflows were replaced with a governed swarm of specialist agents. Failure detection, root-cause analysis and validation across the full collection and intelligence pipeline were automated from day one, with the system designed to progress deliberately from human-in-the-loop oversight towards autonomous execution as each capability proved itself in production. Data assets that had previously required manual retrieval were containerised and made agent-discoverable, and spiders requiring engineering intervention to repair or extend were brought under continuous autonomous monitoring.
The result was a pipeline that did not wait for analysts to find the signal. It surfaced the signal, validated it and delivered it decision-ready — continuously, across every language and data source in scope.
In parallel, the Insights platform itself was modernised — services abstracted, infrastructure containerised, data assets restructured to be agent-discoverable and automation-ready. The intelligence platform was no longer just a repository. It became a living, queryable layer that agents and analysts could work from in real time.
Analyst capacity that had been consumed by search and triage was redirected towards judgement and action. The intelligence that had always existed, buried in the volume, became reachable — and reachable in time to matter.
What began as a workflow fix became a new operating model for how mission-grade intelligence gets produced.
The challenge was never access to data. It was the distance between data and decision.
Billions of Signals. One Question. How Fast Can You Find What Matters?
In high-stakes environments, the speed of intelligence is the intelligence.
~35%
Analyst productivity gain in Year 1 as triage gives way to agent-led workflows
Always-on
Continuous autonomous monitoring across the full pipeline
Days → minutes
Signal to decisionready insight at global scale
200+ languages
Coverage across open, deep and dark web sources
Judgement over search
Analyst capacity redirected from triage to analysis
At one of the world’s largest enterprise software and cloud companies, tens of thousands of sellers were moving as fast as the system allowed. The system wasn’t fast enough.
Six mission-critical internal platforms powered everything from seller compensation and deal intelligence to customer engagement, support operations and product discovery across a global field and partner organisation running billions in annual revenue.
Assembling a view of a high-value account could take the better part of a day. Pay disputes surfaced after compensation had already gone out. Support tickets remained unresolved for days. A marketing campaign operating on a multi-million-dollar budget ran on a planning cycle that took over a month to complete. Meanwhile, the engineering teams responsible for all six platforms powering this sales ecosystem were caught in their own version of the same problem — backlogs that grew faster than they cleared, release cycles that overran by as much as 80% and security, accessibility and compliance debt that compounded quietly across dozens of repositories.
Six platforms. Multiple vendors. Contracts billed by the hour, with no shared line of accountability for what actually changed in the business.
Capacity was always the answer. And capacity was never enough.
Persistent replaced the model with an AI-led, outcomeaccountable delivery approach. A compact AI-led team took end-to-end ownership of the full portfolio, with every commitment tied to a measurable business result and every result visible to the client in real time. Compensation accuracy. Ticket resolution rate. Deal data freshness. These became contractual obligations, evidenced continuously, not estimated in advance and reviewed in arrears.
The first signal came from a pay run. Anomalies were detected before they reached seller accounts. No manual reconciliation. No post-pay escalations. A complete audit trail produced automatically. For a finance organisation that had normalised dispute cycles, the absence of noise was the proof of concept.
From there, the transformation extended across the portfolio. Sellers found a single, continuously refreshed pipeline view replacing hours of manual stitching across disconnected sources. Account signals arrived in time to act on, not days after the window had closed. Support requests that once sat in queues for days were resolved in hours, most without any human intervention at all. A seller seeking the right product configuration could find it without raising a presales request.
The engineering cadence shifted as well. Releases moved faster. Rework dropped. Security, accessibility and compliance debt that had accumulated over years was systematically reduced. Across the portfolio, an always-on observability layer detected anomalies, correlated incidents and generated fix candidates autonomously. Issues that had previously required human triage were being resolved around the clock without one. And throughout, agents earned the right to act without human sign-off progressively — autonomy expanding workflow by workflow only as each outcome was independently verified, with human oversight remaining on every high-stakes decision. The team delivering the work became leaner over time — not because the scope reduced, but because the model was designed to compound.
The engagement began with a portfolio challenge. It became a new standard for AI-led enterprise delivery.
Outcome-priced. Evidence-governed. AI-led. Built to get leaner as it gets better.
Six Platforms. One Team. Sixty-Five Thousand Sellers Moving Faster
Effort was never the measure. Outcome always was.
35–50%
Reduction in overall development cost against the traditional multi-vendor model
~50% faster
Spec-to-delivery across all six platforms
60%+
Tickets autoresolved; MTTR days → hours
10,000+ hrs/wk
Manual effort eliminated across 60+ automated workflows
80%+
Pay accuracy; disputes eliminated at source
30% uplift
Deal velocity across the global sales ecosystem
2x
In drug development, speed and delay are measured in lives as much as in dollars. For one global life sciences organisation deeply involved in clinical research, the pressure to launch new studies faster without compromising quality, compliance or auditability had never been greater. Teams were managing rising study complexity, growing documentation demands and a chronic shortage of expert medical writers.
Every new clinical study begins with an approved protocol — a foundational document defining scientific objectives, methodology, eligibility criteria, endpoints and compliance requirements. Creating these documents was a highly manual, labour-intensive process. Each protocol could require weeks of drafting, dozens of reference checks and multiple review cycles, all dependent on a limited pool of overburdened medical writing experts.
As protocols grew longer and more complex, the bottleneck became harder to ignore. Manual drafting slowed approvals. Reference validation consumed expert time. Review cycles stretched. Missed deadlines created downstream risk for patients, research teams and clinical pipelines.
Without a better model, clinical trial protocol development was becoming an operational choke point.
Persistent was engaged to rethink the process from the ground up. Rather than treating AI as a drafting shortcut, the team collaborated with the client’s medical writers and regulatory experts to understand the full workflow — from study requirements and source references to compliance checks, review cycles and final approval.
The answer was not automation alone. It was a governed AI-assisted authoring system where AI handled the heavy lifting across drafting, research and validation, while domain experts retained control over judgement, nuance and final sign-off.
The solution unfolded in three phases.
First, Persistent built a structured, indexed knowledge repository of previous protocols, scientific literature, regulatory guidelines and institutional best practices, with automated pipelines to keep the content current and compliant.
Second, a domain-tuned GenAI model was developed to generate draft protocol sections from study requirements, pre-populate key content and suggest regulatory language. Every AI-generated section included in-line references and traceability to supporting sources.
Third, automated validation checks were embedded into the workflow to flag missing elements, citation gaps and compliance risks before human review. Feedback from medical writers was captured through governed loops to continuously improve output quality.
The platform was designed for adoption from day one. Medical writers could review, edit and approve content within familiar authoring tools. There was no disruption to daily workflows. Just stronger traceability and more time for strategic oversight.
Piloted on high-priority studies, the solution delivered visible impact within months. Protocols that once took weeks were completed in days. Junior team members could contribute more confidently, guided by AI-enabled consistency and compliance checks. Reusable templates and approved protocol components helped teams accelerate similar studies without duplicating effort.
Persistent brought together GenAI engineering, clinical research domain understanding and regulatedenvironment delivery experience to ensure the platform was not only intelligent, but explainable, auditable and adoption-ready.
Speed, quality and compliance became the default. Not the exception.
Weeks to Days: How AI Accelerated Clinical Trial Protocol Development for a Global Life Sciences Leader
The right AI platform doesn’t just accelerate research. It raises the bar for every study that follows.
60% Faster
Protocol authoring — weeks compressed to days
50% lower
Cost per protocol
40% better
Citation quality
Expanded capacity
Junior writers enabled by AI guidance
At one of the world’s leading biopharmaceutical companies, the challenge was not a lack of digital ambition. It was a need for stronger enterprise architecture. Generative AI (GenAI) experiments were running across R&D, medical affairs and manufacturing. Each one produced something useful. Yet the impact remained fragmented.
Scientific liaisons waited days for evidence-backed answers to complex HCP queries — answers assembled by hand from disconnected document repositories. R&D literature reviews consumed weeks of analyst capacity. Manufacturing deviation handling and clinical data queries required the same slow, manual effort at every turn.
Five departments. Multiple vendors. Promising experiments that had not yet scaled across the enterprise.
The pilots worked. The platform did not exist yet.
Persistent was engaged to help the enterprise move beyond isolated experiments and create a scalable foundation for GenAI. The approach began with a mindset shift: treat GenAI not as a product, but as infrastructure.
Rather than delivering isolated applications, Persistent built a unified, reusable GenAI fabric anchored on Pi-OmniKG — a biomedical knowledge graph co-developed with Google Cloud that connected regulatory, clinical and scientific content into a single contextual intelligence layer.
This was not a single-use assistant. It was a reusable enterprise GenAI layer designed so every new use case could build on the last.
Built-in compliance aligned with HIPAA, GDPR and FDA 21 CFR Part 11 was embedded from day one, not added later.
Medical Affairs became the first testbed. A GenAIpowered assistant was deployed to enable scientific liaisons to respond to complex HCP queries with real-time, evidence-backed answers — complete with citations, confidence scores, regulatory risk flags and a full audit trail.
The legacy workflow had been manual, timeconsuming and risk-prone. The new workflow returned enriched, reference-backed responses in minutes. Medical teams spent less time hunting for documents and more time engaging with physicians. The proof of value was immediate.
As confidence in the platform grew, adoption scaled. R&D literature summarisation, manufacturing deviation handling and clinical data query resolution were all brought onto the same foundation. New use cases are plugged into shared infrastructure rather than requiring a new build. Each one made the next one faster.
The enterprise’s internal AI Centre of Excellence now governs a reusable capability rather than managing scattered pilots. It could build faster, govern better and reuse more. Momentum was no longer accidental. It was designed.
The scattered experiments of yesterday gave way to a unified capability layer. GenAI evolved from theory to transformation.
From Fragmented Pilots to Enterprise Intelligence: How a Global Biopharma Scaled Generative AI
When GenAI has the right foundation, every use case builds on the last.
60%
Reduction in time spent on biomedical content retrieval
45% faster
Speed-to-response for HCP queries
5 departments
Cross-functional reuse of the GenAI fabric
Full traceability
Audit-ready output for every AI-assisted decision
Millions saved
Manual effort reallocated to higher-value work
In retail banking today, consumers expect rates and offers to be responsive, real-time and personalised. Inside the financial institutions delivering that responsiveness, however, the infrastructure often tells a different story. At a leading fintech platform powering pricing and profitability decisions for some of the world's largest banks, that gap had become stark. A customer could receive a ersonalised mortgage rate in seconds, while behind the scenes, updating that same rate in the platform could take six weeks.
Configuration changes were tightly coupled to engineering release cycles. Every update had to be coded, tested and deployed, regardless of complexity. Over time, more than 1,000 legacy issues and 100 outdated components accumulated across five fragmented workspaces.
The constraint wasn't pricing intelligence. It was platform agility.
Persistent was brought in with a singular question: what if routine pricing configuration no longer depended on code?
Using SASVA™, Persistent's Generative AIaccelerated engineering platform, the team decoupled the configuration layer from the core codebase. What was once deeply embedded became accessible through a real-time admin interface. Business users could now make pricing changes instantly, without developer intervention or deployment dependencies.
Configuration updates that previously spanned multiple sprints could now be completed in minutes, directly from the admin panel.
That first breakthrough opened the door to deeper platform modernisation. Over six months, more than 1,000 technical and security issues were resolved, 100+ components were modernised and five siloed workspaces were consolidated into a scalable, unified core.
Developer onboarding time dropped from six to eight weeks to under two. Delivery costs came in at 45% below internal estimates. Release cycles were reduced by 50%. For the first time, pricing control shifted from engineering teams to business users shaping pricing strategy.
The result was a structural reset that enabled the platform to evolve with its users, not behind them.
Once the new configuration engine was in place, the fintech could introduce embedded analytics, smart autocomplete and ML-driven pricing recommendations at scale. What was once a reactive platform became one engineered for continuous iteration.
From Six Weeks to Minutes: Pricing Agility at Scale
Real-time rates for customers, backed by a real-time platform.
6–8 Weeks → Minutes
Pricing change turnaround, from engineering release cycles to real-time admin configuration
<2 weeks
Developer onboarding, down from 6–8 weeks
50%
Faster release cycles
45%
Lower delivery costs vs internal estimates
In retail banking today, consumers expect rates and offers to be responsive, real-time and personalised. Inside the financial institutions delivering that responsiveness, however, the infrastructure often tells a different story. At a leading fintech platform powering pricing and profitability decisions for some of the world's largest banks, that gap had become stark. A customer could receive a personalised mortgage rate in seconds, while behind the scenes, updating that same rate in the platform could take six weeks.
Configuration changes were tightly coupled to engineering release cycles. Every update had to be coded, tested and deployed, regardless of complexity. Over time, more than 1,000 legacy issues and 100 outdated components accumulated across five fragmented workspaces.
The constraint wasn't pricing intelligence. It was platform agility.
Persistent was brought in with a singular question: what if routine pricing configuration no longer depended on code?
Using SASVA™, Persistent's Generative AIaccelerated engineering platform, the team decoupled the configuration layer from the core codebase. What was once deeply embedded became accessible through a real-time admin interface. Business users could now make pricing changes instantly, without developer intervention or deployment dependencies.
Configuration updates that previously spanned multiple sprints could now be completed in minutes, directly from the admin panel.
That first breakthrough opened the door to deeper platform modernisation. Over six months, more than 1,000 technical and security issues were resolved, 100+ components were modernised and five siloed workspaces were consolidated into a scalable, unified core.
Developer onboarding time dropped from six to eight weeks to under two. Delivery costs came in at 45% below internal estimates. Release cycles were reduced by 50%. For the first time, pricing control shifted from engineering teams to business users shaping pricing strategy.
The result was a structural reset that enabled the platform to evolve with its users, not behind them.
Once the new configuration engine was in place, the fintech could introduce embedded analytics, smart autocomplete and ML-driven pricing recommendations at scale. What was once a reactive platform became one engineered for continuous iteration.
From Six Weeks to Minutes: Pricing Agility at Scale
Real-time rates for customers, backed by a real-time platform.
6–8 Weeks → Minutes
Pricing change turnaround, from engineering release cycles to real-time admin configuration
<2 weeks
Developer onboarding, down from 6–8 weeks
50%
Faster release cycles
45%
Lower delivery costs vs internal estimates
A claims adjudicator at one of the leading managed care organisations, specialising in workers’ compensation claims, could spend an entire working week on a single case since the evidence was scattered across the claim record.
30 to 160+ documents per patient — discharge summaries, prescriptions, X-rays, physician notes, each from a different provider, each in a different format. All requiring a human to read, interpret and piece together before a decision could be made.
The bottleneck was architectural. Adjudicators were moving as quickly as the system allowed, but the platform had been built to retrieve documents, while the real need was to reason across them.
Fatigue was inevitable. Details were missed. Outcomes became inconsistent. Reimbursements slowed.
Every unresolved claim added friction, delaying settlement, slowing payouts to providers or patients and eroding trust in the experience.
When every claim requires a week to verify, speed becomes the difference between service excellence and system gridlock.
Persistent co-created an Agentic AI-powered Intelligent Document Processing platform that changed the claims review architecture entirely. Built on DocIntel, Persistent’s agentic document intelligence accelerator, the platform did not simply read documents. It understood them.
Entity extraction identified demographics, injury details, ICD and CPT codes and treatment histories, improving adjudication accuracy by more than 60%. Timeline assembly transformed fragmented records into a longitudinal view of the patient’s treatment journey. Intelligent summarisation produced both note-level abstracts and longitudinal progress summaries across the full continuum of care.
Each claim became a dynamic, interpretable dataset, structured and ready for review in minutes.
The gains did not stop at claims. The same agentic framework is now being extended across broader health and casualty lines, into core enterprise platforms, and across document-intensive workflows in healthcare, legal and financial services.
What began as a claims transformation became a new operating model for enterprise decision-making.
The Claim That Took a Week to Read
AI does not replace expertise. It amplifies it.
95%
Reduction in claims review cycle time
5–7 days
Previous review time
<10 min
With Agentic AI
70%+
Productivity gain
60%+
Accuracy improvement
40%
Throughput increase
A leading European financial institution operates one of the most complex regulatory data environments in global banking. Its compliance and risk functions depend on thousands of ETL pipelines built over decades on legacy Informatica and Teradata platforms, supporting quarterly regulatory submissions and compliance obligations under Basel III, GDPR and European Central Bank reporting mandates.
With data volumes tripling over five years and on-premises licensing costs running into millions annually, modernisation had become a strategic priority. However, migrating without full visibility into the business logic embedded across thousands of legacy pipelines carried significant regulatory and operational risk. Undocumented transformations, opaque data lineage and dependence on scarce specialist knowledge made explainability essential before any modernisation could begin.
Persistent deployed iAURA Assessment, an Agentic AI-powered accelerator designed to decode, document and explain legacy code at scale. Integrated with secure, industry standard enterprise language models, the solution was deployed entirely within the bank's secured environment. No data crossed the enterprise boundary, ensuring alignment with GDPR and internal governance requirements.
The objective was to create a single source of truth for business, compliance, risk and IT teams. iAURA analysed more than 25,000 lines of Informatica and Teradata code, identifying data sources, transformations, controls, dependencies and embedded business logic across regulatory pipelines. Technical logic was translated into natural language narratives explaining what each rule did and why it existed. Multi-agent orchestration generated dynamic flowcharts, sequence diagrams and dependency graphs showing how tables, workflows, transformations and regulatory outputs were connected.
Delivered over eight weeks, iAURA was fine-tuned to the bank's functional context and produced HTML dashboards and structured Excel inventories accessible to non-technical stakeholders across compliance, risk and business teams.
Documentation designed for compliance teams, built by AI.
The impact was significant. A documentation cycle that would typically take four to six months was completed in under six weeks, reducing effort by approximately 70%. Regulatory reviews became more traceable and repeatable. Business-critical logic was preserved and validated before any platform changes were initiated. Reliance on scarce Teradata and Informatica specialists for manual documentation was significantly reduced.
The engagement has established the foundation for a broader modernisation programme. The bank is now evaluating the reverse-engineering of more than 10,000 additional data integration jobs, integration of iAURA with cloud data modernisation pipelines and a governed knowledge base connecting technical metadata with business rules for continuous compliance readiness.
Making Regulatory Data Modernisation Explainable, Traceable and Audit-Ready
Documentation stopped being overhead. It became the foundation for confident modernisation.
~70%
Faster documentation cycles — four to six months reduced to under six weeks
25,000+
Lines of legacy Informatica and Teradata code analysed
8 weeks
Solution delivered entirely within the bank's secured environment
Audit-ready
AI-generated narratives, lineage views and dependency maps supporting regulatory review
BFSI
BFSI
BFSI
COMMUNICATION, MEDIA & TELECOM
COMMUNICATION, MEDIA & TELECOM
Every product transition carries hidden risk. Documentation is never complete. Institutional knowledge lives in the heads of engineers who are already moving on. The window for knowledge transfer is short, the appetite for disruption is zero and the customer on the other end notices everything.
A leading global urban mobility platform needed to transfer ownership of a non-strategic product to Persistent, freeing its core engineering teams to focus on the next generation of its city mobility solutions. The product had grown in complexity over years. Security vulnerabilities needed to be identified, prioritised and built into the transition plan. Critical application knowledge needed to be captured quickly before the client's engineering capacity shifted to its broader roadmap. The transition had to close with zero impact on end customers.
The depth of preparation determined the quality of the handover.
Persistent deployed SASVA™, its Generative AIpowered software assessment platform, before ownership formally changed hands.
SASVA™ ingested the full codebase, mapped the application architecture, surfaced and ranked security vulnerabilities and generated a structured transition backlog. SASVA™ accelerated an assessment that would otherwise have extended the transition timeline, giving Persistent a clear view of exactly what it was inheriting and what needed to be stabilised first.
Persistent presented the findings to the client, walking through the architecture, the vulnerability landscape and the mitigation strategy. The depth of understanding shifted the conversation. This was a team that knew the product before Day 1.
Persistent walked in knowing more than most teams learn in months.
The transition progressed with full continuity of service. Customer experience was uninterrupted. The client's engineering capacity could be redirected towards its broader platform roadmap.
This was the first product takeover in the engagement. The structured, evidence-based approach established the foundation for a longerterm managed engineering relationship, with Persistent positioned to support ongoing product health and evolution.
The question was whether the transition could close without anyone noticing. It did.
COMMUNICATION, MEDIA & TELECOM
COMMUNICATION, MEDIA & TELECOM
CONSUMER TECH
SOFTWARE & HI-TECH
SOFTWARE & HI-TECH
SOFTWARE & HI-TECH
HEALTHCARE
HEALTHCARE
™
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