Retrieval and context
Define approved sources, permissions, revision rules and evidence selection. Test what happens when a relevant source is missing or outdated.
An LLM application includes more than a prompt. It needs source handling, application rules, failure states and a repeatable review process. The model is selected against the task and its data constraints.
Prepare a project briefDefine approved sources, permissions, revision rules and evidence selection. Test what happens when a relevant source is missing or outdated.
Separate answering from changing records. Define tool inputs, authorization, approval gates and failed-action recovery.
Build a representative test set and inspect changes in output quality, latency and cost. Record limitations and escalation behavior.
Does the task need retrieval, fine-tuning or a simpler search interface?
Which actions require a person to approve the result?
How are unsupported answers and source conflicts evaluated?
Review each handoff: what enters, who can act, what can fail and what evidence shows completion.
These answers shape the architecture, estimate and acceptance checks. Record assumptions where an answer is still unknown.
Does the task need retrieval, fine-tuning or a simpler search interface?
Which actions require a person to approve the result?
How are unsupported answers and source conflicts evaluated?
The problem is clear, but the approach is not.
A bounded experiment, findings and a recommendation on what to validate next.
A prototype is not a production-ready service.
You know the workflow and can agree acceptance checks.
Agreed deliverables, verification evidence and operating instructions for the scoped release.
Changes to scope need an explicit decision on cost and timing.
A live system needs focused changes or ongoing care.
A prioritized backlog, reviewed changes and an agreed maintenance or support boundary.
Coverage, response targets and access are agreed per engagement.
Technology is a scoping decision. These are relevant tools and building blocks to discuss against your existing systems, operating constraints and maintenance needs.
Document parsingRetrievalPermissions
Choose source formats and retrieval behavior around the approved material and each user’s access.
Model APIsNode.jsTypeScript
Compare candidate model behavior on representative tasks before committing to a provider or deployment arrangement.
Evaluation setsLogsReview gates
Define an evaluation process and monitor answer quality, latency and operating cost within the agreed privacy boundaries.
Turn quality expectations into reviewable evidence. The exact checks and responsibilities belong in the project scope.
Evaluate answers against expected results and check that cited passages actually support the answer.
Test restricted documents, missing evidence and unsupported tasks; keep consequential actions behind agreed approvals.
Record evaluation cases and limitations so changes to models, prompts or source data can be reviewed before release.
Turn the idea into a scope that can be reviewed and maintained.
Language-model applications with retrieval, source citations, tool boundaries, evaluation and controlled integration into business workflows. The scope can include retrieval and context, tools and application rules, evaluation and operations. Deliverables depend on the agreed workflow and existing systems.
Teams building document assistants, internal knowledge tools or model-assisted product features.
Bring the workflow, user roles, existing systems and anonymized examples. Does the task need retrieval, fine-tuning or a simpler search interface? Start with a local project brief and share it when you are ready.
The estimate depends on the workflows, integrations, data, review requirements and operating responsibilities. These are agreed for the individual project. A planning brief does not book work or establish a delivery commitment.
Bring the problem you want solved. We’ll shape it into a project brief you can keep — or let AI draft a first approach.