Generative AI Development Company
LLM applications and generative AI products taken from idea to production by one accountable studio.
Who this is for
If any of this sounds like you, we should talk.
You are building an AI-first product
The product only exists because LLMs exist. You need a team that treats prompts, evals, and model behavior as first-class engineering, not an afterthought bolted to a web app.
You want generative AI inside an existing product
Summarization, drafting, search, extraction, or a copilot feature inside software your customers already use. It has to match your stack and your quality bar.
Your prototype works, production does not
The demo impressed everyone. Then came hallucinations, latency, cost surprises, and edge cases. You need engineers who have crossed that gap before.
You need a build partner, not a research lab
You want scoped deliverables, a timeline, and a shipped product. Not an open-ended exploration billed by the month.
What changes for you
Outcomes you can point to, not features you can ignore.
- A production LLM application your customers actually use, not a demo that dies in staging.
- A prompt and evaluation pipeline, so output quality is measured and repeatable instead of vibes.
- Model choices made on tested quality, latency, and efficiency for your specific tasks.
- A full product around the model: interface, auth, data layer, and API, all from one studio.
- Code and prompts you own outright, documented for your team.
What is included
Scope, organized by phase.
Discovery (Phase 1 of 3)
Phase 1
Discovery
What we lock down in this phase before moving on.
- Use case definition and output quality bar
- Model landscape testing on your real tasks
- Data sources, retrieval needs, and privacy constraints
- Architecture and stack plan
How an engagement works
From hello to handoff, step by step.
Define the job and the quality bar
We pin down exactly what the AI must produce and how good is good enough, in writing, before any code.
Scoped proposal in 48 hours
Written scope, milestone plan, and fixed deliverables. You know what ships and when before committing.
Prove the core loop
We build the prompt, retrieval, and evaluation core first and test it against real examples. If the model cannot do the job well, you find out in week one.
Build the product around it
Interface, accounts, data layer, and API on our standard stack: Next.js, React, TypeScript, Supabase. The AI core slots into a real product.
Evaluate, harden, launch
Golden test sets, edge case sweeps, output guardrails, then production deployment with monitoring wired in.
Iterate on real usage
Live usage exposes what tests cannot. We review outputs, tighten prompts, and ship improvements on a fixed cadence.
Case study
WitsCode AI Visibility Checker
A live generative AI product on our own domain, built with the same stack and standards we sell. It doubles as our proof: when we say we ship LLM applications, this is one you can go use.
We wanted a free tool that shows a business how visible its brand is inside AI answers. That meant a real LLM application: generating buyer-style queries per domain, evaluating engine responses, scoring brand presence consistently, and presenting it in a report a non-technical owner can read.
We designed the prompt templates, response parsing, and scoring logic, built the orchestration and API routes in Next.js, and shipped it natively inside our own site with lead capture wired into our CRM pipeline. The whole loop runs unattended in production.
Why us
What you get with WitsCode that you don't get elsewhere.
Our own tools are the proof
The AI Visibility Checker on this site is an LLM application we built and run in production. You can test our work before you hire us.
Product engineers first
300+ websites and applications shipped since 2019. A generative AI product is still a product: it needs the interface, data layer, and reliability work most AI shops skip.
Evals over vibes
Every build gets a measurable quality bar and a test suite. You see accuracy numbers on your real tasks, not cherry-picked demo outputs.
WitsCode rebuilt our Shopify store so it finally converts the traffic we were already getting. They understand speed and storytelling in equal measure, and the store has been a real growth lever since launch.
Frequently asked
Questions before you reach out.
A generative AI development company builds software products powered by large language models: copilots, content and drafting tools, document intelligence, AI search, and custom internal tools. That covers model integration, prompt engineering, retrieval pipelines, evaluation, and the full application around the model, from interface to deployment. The output is working software, not a strategy deck.
LLM application development is building production software where a large language model does core work: understanding input, generating text or structured data, or reasoning over your documents. It combines prompt engineering, retrieval (RAG), evaluation pipelines, and conventional product engineering. The hard part is not calling the model, it is making output quality reliable at scale.
With grounding and measurement. We constrain the model to your verified data through retrieval, use structured outputs so responses stay in bounds, add citation or confidence signals where the use case needs them, and run evaluation suites that count factual errors before and after every change. For high-stakes outputs, a human review step stays in the loop.
All of the above, chosen per project. We benchmark candidate models against your actual tasks during discovery and pick on measured quality, latency, privacy requirements, and running efficiency. Builds are model-agnostic at the orchestration layer, so you can switch providers later without rewriting the product.
The core AI loop is usually proven within the first two weeks, and most focused LLM applications reach production in 4 to 10 weeks depending on integrations and evaluation depth. We front-load the risky part: if the model cannot meet your quality bar, you learn that in week one, not month three.
No. We build on API tiers and configurations where your data is not used for provider training, and we document the data path so you can verify it. Where requirements are stricter, we scope private hosting or open source models running in your own infrastructure.
Yes. Embedding generative AI into an existing product is a common engagement: we work in your codebase, match your patterns, and ship the AI feature behind your existing auth and permissions. React, Next.js, and Supabase stacks fit us natively, and we adapt to others.
Ready to ship a generative AI product?
Book a call. We will tell you if the model can do the job, then scope the build within 48 hours.