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Enterprise AI Search Case Study
Kenstin Technologies built a vendor-aware AI Q&A system: answers are validated before they are filed, and retrieval stays fast and correct under multi-tenant filters-so teams trust what the system stores and what it returns.
9
Weeks
100%
Completion
4
Tech Used
Why teams choose this build
Concrete scope signals from the engagement-structured for evaluation, not vanity metrics.
Tenancy model
Vendor-scoped index + APIs
Quality gate
Multi-agent validation
Search layer
OpenSearch + vectors
Project foundation
We start with scope clarity, challenge mapping, and execution guardrails before implementation begins.
Enterprises needed a single place to generate, file, and retrieve Q&A knowledge segmented by vendor, with vector search as the primary access path. The product had to support high write volume (many generated pairs) and high read volume (frequent similarity queries) without cross-vendor leakage. Kenstin delivered an end-to-end pipeline from generation through indexing to filtered retrieval with predictable pagination.
Two problems blocked production readiness. First, a single-shot generation path produced uneven answer quality-sometimes confident, sometimes thin-making filed knowledge unreliable. Second, vendor and question scoping in vector search was inconsistent under pagination, which led to missing hits, duplicated results, or wrong-vendor matches during real queries.
Scope & timeline
Directional highlights for this anonymized portfolio entry-useful for understanding depth of work, sequencing, and ownership.
Delivery window
9 weeks
Tenancy model
Vendor-scoped index + APIs
Quality gate
Multi-agent validation
Search layer
OpenSearch + vectors
Engagement note
The team executed in tightly defined milestones with weekly validation loops, keeping scope, quality, and rollout confidence aligned throughout delivery.
Weeks 1–2
Pipeline diagnosis
Reproduced generation and pagination failures; defined success metrics for answer quality and filtered retrieval.
Weeks 3–5
Generation & validation
Shipped the multi-agent validation path so only vetted Q&A pairs enter the knowledge base.
Weeks 5–7
Search correctness
Hardened vendor filters, vector queries, and pagination semantics at scale in OpenSearch.
Weeks 8–9
Production readiness
Load testing, monitoring, and operational playbooks for index updates and tenant isolation checks.
Execution
Kenstin introduced a quality-first architecture: stabilize generation through validation and refinement before anything is indexed, then harden retrieval for multi-vendor workloads. Prompt orchestration and search-layer behavior were tuned together so the model’s strengths did not fight the index’s constraints.
The team ran repeated failure-mode drills on vendor boundaries and pagination edges to ensure correctness held under production-like traffic.
We implemented a multi-agent pipeline that validates and improves responses prior to filing, reducing bad entries that pollute retrieval. On the search side, OpenSearch-backed vector retrieval used strict vendor-level filters and carefully tested pagination semantics so page boundaries behaved consistently at scale.
We paired this with observability around index freshness and query behavior so operational teams could catch tenant-isolation regressions early.
The system achieved dependable vendor-wise filing, materially more trustworthy answers in the index, and stable paginated retrieval suitable for production traffic. Operations teams could reason about tenant isolation and search correctness-critical for internal knowledge products with SLAs.
The improved reliability reduced firefighting during peak usage and made support escalations more deterministic to resolve.
The stack is selected for reliability, maintainability, and production readiness.
Previous 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.
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