Answers from your own documents, with citations
Retrieval over your data so responses can be checked rather than trusted.
What you get
- Document ingestion and chunking pipeline
- Vector store and hybrid retrieval
- Citation and source attribution
- Freshness and re-indexing strategy
- Retrieval quality evaluation
What changes
- Every answer points at the document it came from
- Updating a policy updates the answers, without a retrain
- “I don't know” is a valid response instead of a plausible guess
When teams call us
- A support team searching a wiki nobody can navigate
- Policy or compliance answers that must be traceable
- A product manual too large to put in a prompt
Process
How the work runs
The same four phases whichever engagement model you pick, each ending in something you can read.
Feasibility
Whether a model genuinely beats deterministic code here, tested on your data before anyone commits a budget.
- Data review
- Baseline comparison
- Go/no-go in writing
Evaluation harness
Built before the feature, not after. Without a scoring set you cannot tell a prompt change from a regression.
- Golden test set
- Scoring criteria
- Regression runs
Build & tune
Retrieval, prompts, tools and guardrails developed against that harness, with cost and latency tracked per change.
- Versioned prompts
- Cost per request
- Latency budget
Ship & monitor
Live with logging of inputs, outputs and tool calls, so quality is observed rather than assumed.
- Output logging
- Drift alerts
- Spend caps
RAG & Knowledge Systems, specifically
The questions that come up on this work in particular.
Still have a question? Talk to the team
Not sure which service your project needs?
Most useful projects cross two or three of them. Describe the outcome and we will map it.