Generative AI with constraints, review and a house style
Generation is the easy half. The work is in the constraints: making output match a schema so it can be stored, match a house style so it can be published, and pass a review step so a mistake does not reach a customer. We build generative systems that produce a draft in your format with the sources attached and a human in the loop wherever the output is outward-facing.
You probably need this if
- Output does not fit the system
- It does not sound like you
- Quality drifts silently
- It published something wrong
Where these projects go wrong
Four failure modes we see repeatedly. Knowing them up front is most of the battle.
- 01
Output does not fit the system
Free-form prose where the database expects fields. Every generation needs a person to reformat it, so nothing is actually saved.
- 02
It does not sound like you
Generic, over-adjectived copy that has to be rewritten anyway. The tone was never encoded, only hoped for.
- 03
Quality drifts silently
A prompt tweak or model update changes the output character, and nobody notices until a customer does.
- 04
It published something wrong
No review step, so a confident, incorrect paragraph went out under your brand.
- Schema-constrained, validated structured output
- Encoded house style and tone guidelines
- Reference examples and few-shot library
- Draft, review and approval workflow
- Batch and bulk generation pipeline
- Quality scoring against a held-out test set
- Version history and rollback of prompt configuration
- Cost and token usage reporting
The engagement, step by step
- 01
Define the output shape first
A schema or template the result must satisfy. Constrained generation is dramatically more reliable than free text you post-process.
- 02
Encode the style from real examples
We build the style guide from writing you already approve of, rather than adjectives in a prompt.
- 03
Score before scaling
A held-out set is scored so quality is a number. Only then does batch generation get switched on.
- 04
Keep a human on the last step
Anything customer-facing is approved before it ships. The saving is in the drafting, and it is still enormous.
Benefits & business outcomes
What changes when this work is done properly — not activity metrics, business results.
- 01
Drafts that fit your systems, not free text
Generative AI development constrained to your schema — validated structured output that flows into databases and workflows without retyping.
- 02
Output that sounds like your brand
House style encoded from real examples you already approve of, rather than adjectives in a prompt that drift with every model update.
- 03
Quality you can score before scaling
A held-out test set produces a number on every prompt change, so batch generation only switches on once quality is measured — not guessed.
- 04
Review where it matters, automation where it does not
Customer-facing content goes through approval; internal drafts ship faster. The saving is in the drafting either way.
The stack for this work
- Anthropic Claude
- OpenAI
- TypeScript
- Zod
- Python
- Next.js
- Queues
- PostgreSQL
- Content and marketing teams generating at volume
- Marketplaces with thousands of listings to describe
- Teams producing repetitive documents or reports
- Products embedding drafting or summarising features
Working remotely with teams in United States, United Kingdom, Canada, Europe, United Arab Emirates, Saudi Arabia, Singapore, Australia, New Zealand and India.
Industries we serve
The same engineering patterns apply across sectors — the domain language and constraints change.
- Marketing and content teams
- E-commerce and marketplaces
- Real estate and property
- Legal and professional services
- Publishing and media
- Recruitment and HR
- SaaS products with embedded drafting
Why choose BuildsInfinity
Practical reasons teams choose BuildsInfinity for generative ai development — grounded in how we actually work, not a slogan list.
- 01
Constraints before generation
Schema, templates and validation come first. Constrained generative AI development is dramatically more reliable than free text you post-process.
- 02
Style from your writing, not a wish list
We build tone guidelines from pieces you are happy with — twenty good examples beat a paragraph of adjectives every time.
- 03
Regressions caught before customers see them
Version history on prompt configuration and a scored test set on every change. A bad tweak rolls back the same way a code change would.
Related services
AI Product Development
Concept to production AI product — model choice, evaluation, cost ceiling and an interface that makes the output checkable.
Agentic AI Development
Agents that use tools and complete multi-step work — inside permission boundaries, with approval gates on anything irreversible.
RAG Development
Answers grounded in your own documents — with hybrid search, reranking, citations and a measured retrieval accuracy score.
Need generative ai development?
Send a paragraph about the problem and you will get a considered reply from an engineer — a shape, a rough timeline and an honest view on whether we are the right fit. Request a consultation or project estimate anytime.