AI DEVELOPMENT
We build generative AI products people actually use - assistants, drafting tools, search and summarization inside your own application. The demo is the easy part. We engineer the retrieval, the tool access, the evals and the guardrails that keep it working in month six.
What generative AI development involves
Generative AI development is building a product feature on top of a language, image or voice model: the interface, the context it is given, the tools it can call, and the checks on what it produces. The model is a component you rent. Everything around it is the product, and that is what we build.
Most generative AI projects die in the same place. A prototype impresses a room in week two, then meets real users who paste in something odd, ask for something adjacent, or believe a confident wrong answer. Closing that gap is not prompt tinkering. It is retrieval quality, tool design, refusal behavior, evaluation and a plan for the cases the model should decline.
We build the feature and the harness that proves it. If you already have a prototype that stalled on the way to production, that is a normal place to start, and often a faster one.
You probably need this if you recognize these
- Your prototype impressed everyone internally and has not shipped.
- Users trust the confident answers, and some of them are wrong.
- Quality changes when the model provider updates something, and nobody notices until a customer does.
- The feature works for the demo case and falls over on the second-most-common one.
- Per-request cost is unpredictable, so nobody will commit to a price for it.
What you get
The feature itself
The interface, the interaction model, and the states nobody designs: empty, loading, partial, refused and wrong.
Grounding
Retrieval over your own content so answers cite a source, built as a knowledge system rather than a prompt stuffed with documents.
Tool access
The actions the feature may take in your systems, each scoped and each logged.
An eval suite
Real inputs, expected behaviors and scored runs, so a change is measured rather than argued about.
Guardrails
Refusal rules, output validation, prompt-injection defenses and a documented failure behavior.
Cost and latency controls
Model routing by task, caching, streaming and a ceiling per request.
How a generative feature is put together
Generative AI development services
Scope
We define what the feature will refuse to do before defining what it does.
Self-healed - retried with fallback tool. Human not required.
Ground
Retrieval is built over your content, with permissions and citations from the start.
Compose
Context, tools and output schema are designed together, not bolted on in sequence.
Evaluate
A scored suite runs against real inputs, including the awkward ones.
Harden
Injection tests, output validation and refusal behavior are checked as features, not edge cases.
Ship
Released behind flags with cost ceilings, live sampling and a rollback path.
Generative features doing real work
Proposal Drafting Agent
Assembles a first draft from your past proposals and the deal record, then hands it to a human to finish.
SEO Content Agent
Researches, briefs and drafts, then routes to an editor before anything publishes.
Knowledge-Base Agent
Answers from your documentation with citations, and flags the questions your documentation cannot answer.
Where the feature plugs in
Generative features usually need three connections: where the content lives, where the user is, and where the action lands.
- Your application and its database
- Document and knowledge stores
- CRM
- Helpdesk
- Content management
- Storage
- Analytics
- Authentication and identity
Platform names are shown as examples of the categories agents connect to. They are not partnerships or endorsements.
What the feature may and may not do
Autonomy boundary
What the feature may produce, what it may send, and what it may only draft. Drafts by default; sending is a decision.
Approval gates
Anything that reaches a customer, moves money, or changes a record of consequence waits for a person.
What stays human
Final judgment on published or sent output, and any request the feature is designed to refuse.
Logging
Prompt, retrieved context, model version, output and user action are logged together, which is the only way to diagnose a bad answer after the fact.
How the work runs
Typical ranges from our engagement model (doc 04 §5), not a quote.
| Stage | Typical | What happens |
|---|---|---|
| Pilot | 3-10 days audit, then 2-4 weeks | Scope, grounding and a first eval suite. If a prototype exists, we score it before changing it. |
| Build | 3-8 weeks | The feature, retrieval, tool access, guardrails, cost controls and observability. |
| Release | 1-2 weeks | Behind flags, to a small cohort, with live sampling before the rollout widens. |
| Managed | ongoing, optional | Evals rerun against new model versions, routing tuned, drift caught before users report it. |
What we measure
- Every release
- Scored against the eval suite before it ships (Target)
- 100%
- Of answers traceable to their retrieved sources (Target)
- Per request
- Cost ceiling set and enforced, not discovered on the invoice (Target)
- Your bar
- Pass rate on your own eval set, agreed before the build (Yours)
“ Until then these are design targets, not results.
What this looks like in practice
Content engine hive
Reference scenario · Professional services. A pipeline that briefs, drafts, reviews and publishes, with an editor in the loop.
Reference scenario - a composite build illustrating our method. Figures are modeled and the model is shown.
Frequently asked questions
Can you take over a prototype we already built?
Yes, and it is one of the most common ways this starts. We score what exists against a real eval set first, which usually shows the problem is retrieval or scope rather than the model. Then we tell you what to keep. Occasionally the honest answer is that a rebuild is cheaper than a rescue, and we would rather say so in week two than bill for six months of patching.
Which model will you use?
Whichever clears your quality bar most cheaply for each task, and we design for that to change. Model choice is a routing decision, not an architectural commitment, because the fast, cheap option a year from now will not be the one you would pick today. Being able to switch without a rewrite is a feature we build in deliberately.
How do you stop it making things up?
Ground it, constrain it and test it. Answers come from retrieved sources with citations, outputs are validated against a schema before they are shown, and the feature is designed to refuse rather than improvise. Then an eval suite scores that behavior on every release. Nothing removes the risk entirely, which is why anything with consequence stays behind an approval gate.
What about prompt injection and data leakage?
Both are treated as security work rather than prompt hygiene. Retrieved content is untrusted input, tool access is scoped per feature, credentials are isolated, and injection cases sit in the eval suite so a regression is caught by a test rather than a customer.
How do we control cost?
By routing each task to the cheapest model that clears its bar, caching what repeats, and setting a hard ceiling per request. Cost per request is monitored the way latency is. Where a smaller tuned model would do the job, that is fine-tuning and evaluation and we scope it separately.
Will it still work when the model provider updates?
Only if you test for it, which is what the eval suite is for. Provider updates change behavior in ways release notes rarely capture. Our suites rerun against new versions before you adopt them, so an upgrade is a decision with evidence rather than a surprise in production.
Related services
Software Development Agents
Reviews, tests, releases and dependency chores, handled.
AI Readiness Audit
Find the processes worth automating before spending on a build.
RAG & Knowledge Systems
Your documents and data, retrievable and cited.