Generative AI development, built into your product
Text, image, and document generation shipped as real product features — with the prompt engineering, guardrails, and evaluation that keep output usable.
output your systems can consume, not just read
- Text generation features
- Document generation
- Structured outputs
- Best when
- A product feature where a generated draft beats an empty field.
- Not a fit when
- Output nobody will review, or generation with no format to validate against.
- First thing you get
- A generative feature with structured output and a test set, running in your product.
What is generative AI development?
Generative AI development is building product features that create new content, such as text, images, summaries and structured documents, with large language and image models. The model is the easy part. Shipping means prompt design, guardrails, evaluation and cost control, so output stays usable across thousands of users. We build those features into your product.
Generative features are easy to prototype and surprisingly hard to ship. The prototype impresses in a demo; the production version has to handle adversarial input, refuse things it should refuse, stay on brand across thousands of outputs, and cost something predictable per user.
We build the parts that make a generative feature durable — structured outputs your systems can actually consume, guardrails on what may be produced, caching that keeps unit economics sane, and evaluation that catches drift.
What this includes
- 01
Text generation features
Drafting, summarising, rewriting, and translating built into your product flows.
- 02
Document generation
Structured documents produced from your data, in your templates and formats.
- 03
Structured outputs
Schema-validated responses your application can rely on rather than parse hopefully.
- 04
Content guardrails
Constraints on what can be produced, with filtering and refusal behaviour you control.
- 05
Cost and caching strategy
Prompt caching and model routing so per-user cost stays predictable at volume.
- 06
Quality evaluation
Test sets and scoring so output quality is measured rather than assumed.
Situations we see most often
Drafting inside your product
Users start from a generated draft rather than an empty field, then edit rather than write.
Summarising long records
Condensing case histories, threads, or reports into something scannable.
Personalised communication at scale
Generating tailored messages that stay within approved bounds.
Turning data into narrative
Converting numbers into written commentary for reports and dashboards.
What to know before you start
Which product features benefit from generative AI?
Features where a good draft beats an empty field: replies and messages, summaries of long records, reports and documents assembled from data, product descriptions, and personalised communication at scale. The user stays in control, editing rather than writing from scratch.
Weaker candidates are outputs nobody will review and generation with no structure to check against, because there is no way to tell when quality slips.
How do you control the format and content of the output?
Where structure matters, outputs are constrained to a schema, such as JSON with defined fields, and validated before use, so the rest of your system can rely on them. Content guardrails and refusal behaviour cover what the model must not produce, and prompts carry your tone and terminology.
All of it is checked against an evaluation set that includes the awkward and adversarial inputs real users will send.
How do you keep running costs predictable?
By modelling cost per user during design, before the first surprising bill. Prompt caching avoids paying repeatedly for the same context, model routing sends simple requests to smaller models, and output length is controlled.
Usage and cost are tracked per feature in production, with limits that stop runaway usage from becoming an unbudgeted expense.
Can generated content be checked before it reaches users?
Yes, in proportion to the risk. Low-risk drafts go straight to the user who edits them; customer-facing or regulated content can pass through validation rules, a second model check, or human approval before it is sent. The level of checking is a design decision we make with you per feature.
How is generation shown to users?
Usually streamed, so text appears as it is written instead of after a long wait, which makes even longer outputs feel fast. Users can stop, regenerate or edit the result, and the interface makes clear what was generated so they review it rather than trust it blindly.
Good interface design matters as much as the model here: a generative feature people do not trust or cannot control goes unused.
What drives the cost of generative AI development?
Prototypes are cheap; production features are not. These factors decide the difference.
| Factor | Effect | Why it matters |
|---|---|---|
| Output quality bar | High | Higher quality and consistency needs more prompt work, evaluation cases and iteration. |
| Structured output and validation | Medium | Schemas, validation and fallbacks for invalid output add engineering, and make the feature reliable. |
| Guardrails and safety | Medium | Content filters, refusal behaviour and adversarial testing scale with how public the feature is. |
| Integration into your product | Medium | Streaming interfaces, editing flows and storing outputs are product work beyond the model call. |
| Volume and model choice | Medium | Running cost follows usage and model; caching and routing reduce it substantially. |
| Self-hosted models | Low | Deploying open-weight models in your infrastructure adds setup, and removes per-call fees. |
What a typical engagement looks like
- 011–3 weeks
Discover
Process map
- 022–4 weeks
Design
Interactive prototype
- 03two-week cycles
Build
Working software each cycle
- 04ongoing
Ship & support
Automated pipeline
What we build it with
- Models
- ClaudeGPTOpen-weight models
- Control
- JSON schemasGuardrailsPrompt caching
- Delivery
- StreamingYour templatesEvaluation sets
Questions we get asked
01How do you control what the model produces?
Through schema-constrained outputs where structure matters, explicit content guardrails and refusal behaviour, and evaluation against a test set that includes the adversarial inputs you expect to see.
02What does it cost to run at scale?
It depends on volume and model choice, and it is the question teams most often skip until the first surprising bill. We model it during design and use prompt caching and model routing so the per-user figure is predictable.
03Can we use our own or open-weight models?
Yes. Where data residency or cost demands it, we deploy open-weight models in your own infrastructure instead of calling a hosted API.
04Will our data be used to train the model?
No. We use enterprise API tiers where inputs are not retained for training, or open-weight models in your own infrastructure for sensitive data.
05Can outputs follow our templates?
Yes. Generation can fill your own document and message templates, so the output looks like yours and only the variable parts are generated.
06Which models do you use?
Claude, GPT or open-weight models, chosen per feature on quality, speed and cost. Features are built so the model can be changed later, with the evaluation set confirming quality holds.
07How long does a generative feature take to build?
A first version of a focused feature, with structured output and an evaluation set, is often a matter of weeks. The time goes into evaluation and guardrails, not the model call.
08Can generated text be translated or localised?
Yes. Generation can work directly in Indian and international languages, or produce text in one language and translate it, with evaluation in each language you serve.
Adjacent capabilities
Tell us what you are building.
Share the problem and we will come back with an approach, a rough shape of the work, and what it would take to deliver it.