An AI demo becomes useful software when its inputs, limits and review process are explicit. We help teams connect approved data, define the task and evaluate outputs against realistic examples before expanding access or automating decisions.
Prepare a project briefConnect approved sources, normalize records and preserve source references through ingestion and retrieval. Define refresh schedules, deletion behavior and access filters. Show users where answers come from and handle missing or conflicting material without treating every generated response as established fact.
Build task-specific evaluation sets with representative examples and difficult edge cases. Compare retrieval quality, output accuracy, latency and cost against agreed acceptance criteria. Route uncertain or consequential outputs to reviewers, and retain enough context to investigate regressions after a change.
Embed AI features in existing tools with explicit permissions, usage limits and fallback behavior. Separate suggestions from actions that change business records. Plan model updates, monitoring and feedback ownership so the team can operate the feature beyond its first demonstration.
Which task and evaluation examples define an acceptable result?
Which sources may each user access, and how are updates or deletions propagated?
Which outputs require review before they trigger a business action?
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.
Which task and evaluation examples define an acceptable result?
Which sources may each user access, and how are updates or deletions propagated?
Which outputs require review before they trigger a business action?
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.
AI product development, data pipelines, retrieval systems and evaluation workflows grounded in source ownership and measurable acceptance criteria. The scope can include data foundations and retrieval, evaluation and human review, product integration and operations. Deliverables depend on the agreed workflow and existing systems.
AI product companies and data teams turning documents, operational records or model experiments into usable applications.
Bring the workflow, user roles, existing systems and anonymized examples. Which task and evaluation examples define an acceptable result? 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.
