The interesting generative AI work is rarely a standalone chat window. It is the summary at the top of a case file, the first draft in the reply box, the classification that routes a ticket before anyone reads it — features that sit inside software people already use and remove a step they already resent.

Who this is for

Product teams adding intelligence to an existing application. Companies whose staff spend hours producing text that follows a predictable pattern: summaries, replies, descriptions, reports. Anyone who has tried a generative feature and found the output almost right but not reliably enough to ship.

Problems we solve

Almost-right output. Good enough to demo, not consistent enough to put in front of a customer.
Prompt regression. A change that fixes one case and silently breaks four others, because nothing is measured.
Unstructured responses. Free text where the application needed valid, parseable JSON.
Latency in the wrong place. A user staring at a spinner because generation was put on the critical path.
Unbounded cost. A feature whose spend scales with usage and has no ceiling.

What we build

Drafting and rewriting features that adopt your tone and formatting rules
Summarisation over long documents, threads and call transcripts
Classification and routing — intent, sentiment, priority, category
Structured extraction with schema validation and repair on malformed output
Semantic search and natural-language query over your own data
Streaming interfaces so responses appear progressively rather than after a wait

How we work

Every generative feature ships with an evaluation set — real inputs with expected characteristics — that runs in CI. That is what makes prompt changes safe to make. We constrain output with schemas rather than trusting instructions, cache aggressively where inputs repeat, and set explicit spend ceilings with graceful degradation when they are reached. Where a smaller model is sufficient, we use it; premium models are reserved for the steps that genuinely need them.

Technologies we use

OpenAI, Anthropic Claude, Google Gemini and self-hosted Ollama, accessed through a provider abstraction so a model swap is a configuration change. Structured output with JSON schema validation, Redis for caching and rate limiting, and streaming over server-sent events into React and Next.js front ends.

Business benefits

Repetitive writing collapses from minutes to seconds per item
Output tone and structure stay consistent across a whole team
Backlogs of unsummarised or unclassified material get cleared
Evaluation in CI means quality is verified before release, not after complaints

Where it pays off

Support replies drafted from ticket history and product documentation
Meeting and call transcripts turned into summaries and action items
Product descriptions and listings generated from structured attributes
Incoming email and form submissions classified and routed automatically
Long reports and case files reduced to a readable brief

Common questions

Which model should we use?

It depends on the task, and we benchmark rather than assume. Classification and extraction often run well on small cheap models; nuanced drafting usually needs a stronger one. We build so the choice can change later without touching application code.

How do you keep output on-brand?

Examples beat instructions. We tune with real samples of your existing material, encode formatting rules as validated structure, and measure adherence on an evaluation set rather than judging by eye.

What if the model returns malformed data?

Schema validation catches it, a repair pass attempts a fix, and a deterministic fallback handles the remainder. The application never receives unvalidated output.

Have a feature that is almost good enough? Usually the missing piece is evaluation, not a better model.

Generative AI Applications