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HealthTech Case Study
Kenstin Technologies delivered a Private GPT–style Q&A layer for sensitive domains: encrypted handling, tight access controls, and answers grounded in approved sources-aligned with GDPR and SOC 2–style expectations for how data is stored, processed, and audited.
14
Weeks
100%
Completion
4
Tech Used
Why teams choose this build
Concrete scope signals from the engagement-structured for evaluation, not vanity metrics.
Compliance framing
GDPR + SOC 2–style
Grounding model
Approved sources + policies
Hosting posture
AWS + segmented services
Project foundation
We start with scope clarity, challenge mapping, and execution guardrails before implementation begins.
The engagement focused on a secure conversational assistant for internal teams handling regulated or confidential information. The product needed to feel as capable as consumer assistants while defaulting to least-privilege access, explicit retention boundaries, and traceability suitable for security reviews. Kenstin scoped the system around document-grounded responses, role-aware usage, and operational controls that security stakeholders could reason about.
The client required natural-language Q&A without expanding the attack surface: data had to stay segmented, prompts and outputs had to be governable, and workflows had to map to GDPR and SOC 2–aligned practices (minimization, access control, and accountability). Generic chat APIs were not acceptable without guardrails, and the experience still had to be fast enough for daily use.
Scope & timeline
Directional highlights for this anonymized portfolio entry-useful for understanding depth of work, sequencing, and ownership.
Delivery window
14 weeks
Compliance framing
GDPR + SOC 2–style
Grounding model
Approved sources + policies
Hosting posture
AWS + segmented services
Engagement note
The team executed in tightly defined milestones with weekly validation loops, keeping scope, quality, and rollout confidence aligned throughout delivery.
Weeks 1–3
Threat modeling & requirements
Aligned on data classes, retention boundaries, access roles, and audit expectations with security stakeholders.
Weeks 4–8
Core retrieval & generation
Implemented LangChain-style orchestration with grounded responses, governance hooks, and conservative failure modes.
Weeks 9–12
Privacy hardening
Tightened logging, segmentation, and operational controls so prompts and outputs remained governable end to end.
Weeks 13–14
Launch & handoff
Production cutover support, runbooks, and guidance for extending the assistant to additional internal use cases.
Execution
We engineered privacy-first by design: secure transport and storage, strict service boundaries, controlled access patterns, and clear separation between retrieval, generation, and logging. Conversational UX was tuned for professional contexts-citations where appropriate, conservative defaults when uncertain, and configurations that allowed the client to tighten policies over time without re-architecting the product.
We treated compliance reviews as part of delivery cadence, with explicit checkpoints for retention behavior, access controls, and escalation policy fit.
The solution combined natural-language understanding and grounded response generation with policies that limit what leaves trusted environments. Adaptive behavior was implemented in ways that do not undermine privacy expectations: learning signals were scoped, auditable, and compatible with the client’s data handling model.
The result was an assistant-shaped interface that security and compliance stakeholders could stand behind. Supporting controls included role-aware service boundaries, auditable event trails, and deployment practices aligned to least-privilege operations.
The client shipped a production-ready, privacy-centered Q&A capability that improved how teams accessed institutional knowledge while staying inside regulatory guardrails. Completion hit the full agreed scope, with a foundation the organization could extend to additional use cases without compromising its security posture.
Security and compliance stakeholders gained clearer evidence for internal reviews, reducing friction in future rollout decisions.
The stack is selected for reliability, maintainability, and production readiness.
Previous case study
Kenstin Technologies built Corofy end to end: a full-stack B2B sales intelligence platform that unifies lead discovery, GTM analysis, deep company research, document workflows, and batched email execution-so revenue teams can research, decide, and act without switching tools.
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