Cut research time in half. Get answers from your data — not hallucinations.
Your team spends hours searching documents, Slack threads, and wikis for answers that already exist. We build copilots that retrieve the right information, cite their sources, and flag uncertainty — so people make faster decisions with higher confidence.
Typically uses Azure OpenAI, Azure AI Search, and your choice of vector store (Qdrant, Weaviate, pgvector).
What’s included
Embedding pipeline
Automated chunking, embedding, and indexing of your documents. Configurable strategies (fixed-size, semantic, recursive) tuned for your content — not a one-size-fits-all default.
Hybrid retrieval
Vector similarity + keyword search + reranking. Retrieves the most relevant context, not just the most similar embedding — measurably better hit rate than vector-only search.
Prompt engineering
System prompts, few-shot examples, and citation formatting tuned for your domain. The copilot sounds like your team, not a generic chatbot.
Schema-validated outputs
When downstream systems need structured data, responses conform to a JSON Schema. No free-text surprises breaking integrations.
Eval harness
Faithfulness, relevance, and answer-quality metrics computed on golden datasets. Runs in CI on every PR — hallucination regressions block the deploy.
Deploy anywhere
Chat UI, API endpoint, or embedded in your existing app. Whatever fits your team's actual workflow — not whatever's easiest for the vendor.
Our tooling approach
Tool allowlists
If your copilot can call tools (search, database lookups, APIs), each tool is explicitly allowlisted with typed schemas. The LLM cannot invoke unapproved operations.
Retries & fallbacks
Retrieval failures trigger automatic retries. If the index is unavailable, the copilot surfaces a graceful “I can’t answer right now” message instead of hallucinating.
Audit logging
Every query, retrieved chunk, prompt, and response is logged with tracing IDs. Full audit trail for compliance and continuous-improvement analysis.
Quality & evaluation
Faithfulness scoring catches hallucination
Automated checks verify every answer is grounded in the retrieved context. Fabricated claims are flagged before they reach users — not discovered in a customer-facing incident.
Regression + red-team tests in CI
Golden answers, edge-case queries, and adversarial prompts run on every PR. If accuracy drops or the model leaks data, the deploy is blocked. Same rigor as your unit test suite.
Live dashboards you can show your CISO
Retrieval-hit rate, answer latency, user satisfaction signals, cost per query, and drift detection. Alerts fire automatically — you see problems before users file tickets.
Data & privacy
- Permissioning: document-level access controls ensure users only retrieve content they are authorized to see.
- PII handling: configurable PII detection and redaction in both the indexing pipeline and the response layer.
- Data boundaries: embeddings and indexes live in your Azure subscription. Your data never leaves your tenant.
Timeline & investment
Blueprint
10 days
Data audit + architecture
Build
3 – 6 weeks
MVP to production
Investment
$25K – $90K
Depends on data volume
What we need from you
- • Access to the document corpus or data sources the copilot will use
- • Subject-matter experts to validate golden answers for the eval suite
- • A product owner who can define scope and prioritize user scenarios
- • Weekly 30-minute check-ins during the build phase
Security & guardrails your CISO will approve
Every AI system we ship includes these controls — in the first deploy, not a future phase.
Tool-call allowlists
The AI can only call tools you explicitly approve. Every external integration is registered with typed schemas — no unapproved operations, no unstructured side effects.
Schema-enforced outputs
Every response to a downstream system is validated against a JSON Schema before delivery. Malformed output is caught and logged, not silently propagated.
Eval suites in CI/CD
Regression tests, red-team prompts, and accuracy benchmarks run on every pull request. If eval scores drop below threshold, the merge is blocked.
Production observability
Latency P50/P95, token costs, error rates, and output drift — all in dashboards with configurable alerts. You see problems before users report them.
Human-in-the-loop gates
Configurable confidence thresholds route low-certainty decisions to a human reviewer before execution. The threshold is tunable without a code deploy.
Immutable audit trail
Every LLM call — inputs, outputs, token counts, tool invocations, cost, latency — is logged in an append-only store. Ready for compliance review or incident forensics.
Stop funding pilots that never ship.
A 10-day paid Blueprint gives you an architecture doc, risk register, costed backlog, and ROI model — artifacts you own and can act on immediately.
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