Generative AI, wired into the business
Language models put to work inside your product, grounded in your data, guarded by evaluation suites, and priced for production rather than demos.
What should we check before releasing a new AI feature?
Evaluation · Permissions · Fallbacks
Use the relevant information. Keep missing context visible.
Review the task-based tests, access boundaries and fallback behavior. Confirm who handles feedback after launch and record any known limitations.
The product owner checks the evidence before approving release.
Start with one useful product moment.
The gap between a generative AI demo and a generative AI feature that earns its place is engineering: retrieval that cites sources, prompts versioned like code, evaluations that catch regressions, and inference bills that stay explicable. That gap is where we work.
What the work includes
We integrate language models into real products, copilots beside your users' workflows, knowledge systems over your documents, content pipelines with human approval gates, on whichever model earns the job, abstracted so the frontier can move without breaking your product.
Build AI into a useful product moment.
Explore the kinds of work this practice covers. Every project starts with its own requirements and review criteria.
RAG knowledge systems
Retrieval over your documents with sources attached, answers users can verify and trust.
Product copilots
Assistants embedded beside real workflows, acting with permissions and escalating deliberately.
Content pipelines
Drafting, tagging and localization flows with human approval gates and full revision history.
AI search
Natural-language search over structured and unstructured data, ranked for your domain.
Model integration & abstraction
Provider wiring that stays swappable, evaluations decide the model, not the marketing.
Cost & safety engineering
Inference budgets, caching, rate governance and guardrails tuned for production traffic.
Useful intelligence needs clear boundaries.
The model is only one part of the system. Explore the information, permissions and review paths around it.
Relevant information
Select useful sources, respect access and make the supporting context available. Missing or stale information should be visible, not silently filled in.
Bounded actions
Specify what the system may read, prepare or change. Sensitive actions need the right permission and, where appropriate, a person's approval.
Evaluation & fallback
Test routine questions, incomplete inputs and difficult cases. Define when to ask, stop, retry or hand off rather than assuming every answer is correct.
An operating plan
Agree who reviews usage, handles incidents and maintains prompts, data or integrations. Monitoring, cost controls and review records depend on the deployment.
AI is not always the answer.
Clear rules may be simpler. Thin information may need attention first. For sensitive decisions, AI can prepare context while a qualified person decides.
Explore AI strategyA clear path from idea to operation.
Start with a focused scope. Make the information, evaluation and operating responsibilities part of the work.
Ground
Retrieval and context built over your real data, with permissions scoped correctly.
Guard
Guardrails, approval gates and evaluation suites that measure quality continuously.
Integrate
The model wired into your product surfaces, not a chat box bolted to the side.
Improve
Traces, feedback loops and a tuning cadence that compounds instead of drifting.
Illustrative delivery stages. Scope and timing are agreed for your project.
The tools follow the task.
Models, platforms and frameworks are selected around your information, team and operating requirements.
OpenAI
Anthropic- RAG
- Vector search
- Evaluations
- Guardrails
Find the right kind of AI work.
An agent, a connected workflow, a product feature or a data model. The starting point depends on the problem.
A few things worth asking.
Practical answers about scope, information and what happens after the first build.
The one that earns it on your tasks. We evaluate options for your workloads and wire the abstraction so models swap as the frontier moves, your product never marries a vendor.
Grounding first: answers come from your data with sources attached, confidence gates route uncertainty to humans, and evaluation suites catch regressions before users do. No system is perfect; this one is accountable.
It is engineered, not discovered: caching, model tiering and prompt budgets keep costs explicable. We report inference spend as a product metric from the first release.
Yes, most of our work lands inside existing products. The integration layer respects your architecture instead of demanding a rewrite.
AI project questions
Start with the work you want to change.
Tell us the task, the systems involved and where your team needs help. We’ll work out whether AI, automation or a simpler approach fits.