Generative AI Development

A generative AI development company that tests on your data before launch

We build LLM products that run in production: answers grounded in your documents, agents that work inside your tools, and generative features in a product you already run. Each build starts with a test set of your real examples, so you see how it performs before anyone relies on it.

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Your code. Your infrastructure. Production guardrails. Built around your workflow. One accountable engineering team.

What a generative AI development company builds

What it is

A generative AI development company builds software where a large language model writes, answers, summarises or decides as part of the product. In practice that means three kinds of work: retrieval systems that answer from your own documents, agents that read and act in your tools, and LLM features shipped inside an existing product.

The model is the easy part to buy. What takes the work is everything around it: which documents it can see, which actions it may take, what it costs per run, and how you know an answer is right. We write those down first, collect real examples with the answers a good result should give, and test every version against them.

We do not train foundation models. We build on Claude and OpenAI models through your approved accounts, chosen per task after testing, in TypeScript and Python. If a non-generative approach or an off-the-shelf tool does the job better, we say so.

Comparing options before you commit? How to choose a generative AI development company.

Is this the right fit?

Fit check

Where this wins

Teams sitting on documents nobody can search

Contracts, policies, tickets or manuals turned into answers with the source attached.

Products that need a generative feature

Drafting, summarising, chat over account data or classification shipped inside the live product.

Founders building an LLM-native product

Architecture through to a working product, with evaluation in place from the first version.

Teams sitting on documents nobody can search Contracts, policies, tickets or manuals turned into answers with the source attached.
Products that need a generative feature Drafting, summarising, chat over account data or classification shipped inside the live product.
Founders building an LLM-native product Architecture through to a working product, with evaluation in place from the first version.

When NOT to use this

You want a foundation model trained from scratch. That is research lab work, and we do not do it.
The job needs exact, repeatable outputs every time, such as a tax calculation. Ordinary code is cheaper and safer there.
There are no real examples to test against and no one who can judge a good answer. Without that, nobody can show the system works.

How a generative AI build runs

How it works

The test set comes before the build, so every version is judged on the same real examples.

Typical timeline

A focused build, scoped after the discovery call

Stack we build with

Claude · OpenAI · TypeScript · Python · Next.js · PostgreSQL · Supabase · n8n

// 01

Define the use case

Write down what the system must produce, for whom, and what a wrong answer costs.

DeliverableOne-page spec and success measures

// 02

Build the test set

Collect real inputs from your documents, tickets or product, with the answers a good result should give.

DeliverableTest set

// 03

Build the first version

Retrieval, prompts and tools wired to your systems in a sandbox, with the model chosen by testing.

DeliverableWorking first version

// 04

Test and tighten

Run the test set, fix retrieval and prompt failures, and add human approval where errors are costly.

DeliverableTest report

// 05

Release and monitor

Go live with logging, cost tracking and alerts, then review the first weeks of real use.

DeliverableLive system and monitoring

Not sure an LLM is the right tool?

We tell you straight if ordinary code or a product you can buy does the job. If not, we scope the smallest build that proves the value on your data.

What we build most often

Use cases

Related client work

Customer Support Automation

24/7 Resolution

Creative Automation

4h → 5min Asset Production

Legal & Compliance Automation

Saved €10K in Fines

Answers from your documents

RAG systems that answer staff or customer questions and cite the source document.

Agents that work in your systems

Agents that query a database or CRM and produce reports, limited to what each person may see.

Multilingual document tools

Reading and summarising documents in the languages your users work in.

What your first 30 days look like

First 30 days

A spec, a test set and a first version answering real questions in a sandbox.

// 01

Spec and success measures

Week 1

The use case in plain language and the numbers we will judge it by.

// 02

Test set

Week 2

Real questions or inputs, with the answers a good result should give.

// 03

First working version

Week 3 to 4

Connected to your documents and systems in a sandbox, not yet live.

// 04

First test report

Day 30

Where it answers correctly, where it fails and what we change next.

Scope and exclusions

Scope

In scope

Use case definition and the measures it will be judged by
Test set built from your real examples
RAG pipeline, agent or LLM feature built on your data and systems
Model choice per task, with cost per run estimated
Logging, alerts and a monitoring plan, then handover or ongoing maintenance

Not in scope

Training foundation models from scratch
Image or video model research
Accuracy guarantees on data we have not tested
Chatbots with no defined job or owner

How this is priced

Pricing

Priced per project and scoped after a short discovery call, not sold as a fixed package. Cost follows the number of systems, the access model and the security requirements.

Scoped build

Scoped after a discovery call

Most engagements start with one narrow, high-value piece so you see it running in production before anything expands.

Embedded engineer

From $60,000/year

A dedicated engineer building and maintaining the work inside your team, instead of a scoped project.

Book a Call

Talk to an AI engineer

Choose a time for your free 30-minute strategy call. Live availability is shown in your timezone.

Book a 30min Free Strategy Call

In this call, we?ll walk through your project scope, timeline, and goals?so we can both check if we?re a fit. No obligation, no slide deck, just a working session.

Don?t want a call? Email info@manasflow.com

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Before we build

Generative AI Development questions

Practical answers about scope, delivery, ownership, and working with ManasFlow.

What does a generative AI development company do?

A generative AI development company builds software where a large language model produces the output: answers from your documents, drafted text, summaries or actions taken by an agent. The work covers the use case, the data the model may see, testing against real examples, and running the system in production with logging and cost tracking.

How do I choose a generative AI development company?

Ask four things. Will they build a test set from your real examples and show you the results? Can they show a system that is still live in production? Who owns the code and the model accounts? How do they handle a model update from the provider? Our answers: a test set in week 2, code in your repositories, and monitoring plus an optional maintenance plan after release.

Is this different from custom AI development?

This page covers products built on large language models: RAG, agents and generative features. Our custom AI development company page covers the wider set, including matching, prediction and internal tools that may not use an LLM at all.

Do you train or fine-tune models on our data?

Usually not. We use retrieval, tools and prompts against your data through approved model routes. Fine-tuning is a separate decision and only makes sense when testing shows it helps.

Which models do you use?

Mostly Claude and OpenAI models, chosen per task after testing on your examples. A smaller, cheaper model often handles routing or classification steps, with a larger one where the answer quality needs it.

What does it cost?

Scoped after a discovery call, because cost follows the systems, the data access and the review steps. Use the AI agent cost calculator to estimate running costs. Placement of an embedded engineer starts from $60,000/year.

Who owns the code and the data?

You do. We build in your repositories and cloud accounts, use your model accounts, and hand over documentation and a runbook.