Production‑ready AI agents in six weeks.

We find the workflow. We build it with your team. We stay until it runs in production.

Deploy puts AI into production inside your company

Stanford Healthcare

IIT Bombay

mem0

Most companies have the tools. Few have the results.

The difference is deployment. Our forward-deployed engineers own one workflow from the first conversation to production, inside your company.

Week 1

Find

We map your workflows step by step and find where AI pays off. You leave with one use case, an owner and a target number.

Weeks 2 to 4

Build

Our engineers work in your cloud and your repos. A prototype in 3–4 days, then daily iteration with real users until it clears 95%.

Weeks 5 and 6

Ship

It runs in production by week six. Your team gets the code, the evals and the training to keep it running.

Why AI stalls

Lots of pilots. Very little in production.

Everyone has seen a great demo. The hard part is everything between that demo and a system people rely on every day.

1 in 20

custom enterprise AI projects make it all the way into production.

Nearly half

of AI proofs of concept are scrapped before they ever reach production.

3×

more likely to succeed when built with a specialist partner than alone.

Our promise

Production-ready in six weeks. Not a pilot that lingers.

Most in-house AI pilots spend weeks reaching a demo, then stay there. We work to a different clock: a working prototype in days, and a system your people use every day by week six.

A typical in-house pilotwhat we usually walk into

Week 6

A pilot, after 6 weeks

…and it never reaches production

With Build Fast with AIwhat we promise

Iterate with users

Harden

In production, used every day

3–4 daysto a working prototype on your real data

Week 6in production, with evals and monitoring on

Yourscode, evals and a trained team at handover

What production takes

What you see in a demo. What it takes to ship.

Everything under the waterline is the work our engineers do with your team.

The demoA prompt, a clean interface, the happy path.

Evals on your real dataAccuracy measured on your cases, not demo cases.

Edge cases and failure handlingWhat happens when the input is messy.

Guardrails and human reviewA person approves the risky steps.

Integration with your systemsCRM, portals, inboxes, databases, permissions.

Monitoring and cost controlCatch drift before your users do.

A team that can own itYour engineers trained to run and extend it.

Why it's hard alone

Everyone wants AI. Few teams can implement it yet.

Reason one

They don't know where to build.

Leadership is sure AI can help. They're less sure what it can do today, or which workflow to start with.

Reason two

Their teams can't build it yet.

A backend engineer with 15 years of experience is new to agents, like everyone else. The stack changes every few months, on top of the day job.

	Your existing dev team	A specialised AI team
Up to date on agents, MCP and evals	✕Learning on the side	✓It's the whole job
Has shipped AI to production	✕Often the first time	✓Many times, across companies
First working prototype	✕Weeks to months	✓3–4 days
Evals and guardrails	✕Added late, if at all	✓From day one
Knows which use cases pay off	✕One company's view	✓Patterns from 100+ companies
Focus	✕Shared with the existing roadmap	✓Dedicated until it ships
Where it ends up	✕Stuck at the pilot	✓In production in six weeks

We don't replace your team. Your engineers build alongside ours and keep the system when we leave.

The six weeks

One workflow. Six weeks. In production.

You see progress every day, and accuracy is measured against a number we agree in week one.

Illustrative. Each week adds a layer, and the gains compound towards week six.

Week 1

Pick the workflow

A 2-day workshop with leadership and teams. We write the workflow down, agree the target number and get access.

Week 2

Prototype on real data

A working version on your data in 3–4 days, plus evals from your real cases.

Week 3

Iterate with users

Real users try it every day. We fix, measure and repeat.

Week 4

Harden it

Errors, retries, edge cases, guardrails and connections to your live systems.

Week 5

Run it in production

Side by side with today's process. A person approves risky steps.

Week 6

Hand it to your team

Docs, playbooks, training and tests you can re-run. You own the code.

What we build

Whatever the workflow needs, built to production standard.

We pick the approach after we've seen the workflow, not before. These are the systems we build most often.

AI agents

Agents that plan, call tools and finish multi-step work, with a person approving the risky steps.

RAG and knowledge

Answers grounded in your documents and data, with sources a reviewer can check.

MCP and tool integrations

Connect models to your CRM, portals, inboxes and internal APIs, with the permissions you set.

Document processing

Pull fields from invoices, contracts and forms, check them against your rules, flag what doesn't fit.

Data analysis agents

Agents that query your warehouse, run the analysis and explain the numbers in plain English, checked against your own metric definitions.

Voice agents

Live voice conversations for screening, intake and support, with handover to a person.

Workflow automation

The repetitive, multi-step work between systems, done end to end and logged.

Evals and guardrails

Test suites built from your real cases, so you know the accuracy before anyone relies on it.

Models

ClaudeGPTGeminiDeepSeekQwenMistralGLMKimi

Agent frameworks

Claude Agent SDKOpenAI Agents SDKLangGraphCrewAIMCPAgno

Data and retrieval

PostgrespgvectorPineconeSnowflakeBigQueryQdrantmem0

Cloud

AWSAzureGoogle CloudVercelYour own servers

Your tools

SalesforceHubSpotSlackMicrosoft 365Google WorkspacePower BI+ hundreds more

These, plus hundreds of other tools and APIs your team already uses. If it has an API, we can connect it.

Built inside teams in energy, consulting, venture capital, finance, HR tech, education and enterprise software.

Who shows up

A small team that sits with yours.

Product and process

Learns your workflow and turns a vague request into a number we can measure.

AI and integration

Builds the agents, retrieval, model calls and connections to your systems, in your repo.

Evals and operations

Adds the evals, permissions, logs and failure handling that make a demo dependable.

100+companies supported with AI builds and transformation

75,000+people trained by Build Fast with AI

500+workshops delivered for teams

Your data

Your data stays inside your walls.

Built in your environment

We work in your cloud, your repos and your ticketing tools. Nothing is rebuilt somewhere else.

Your access controls

Our engineers use the permissions you grant, and data stays where it already lives.

You own everything

The code, the evals and the documentation are yours from day one.

FAQ

Questions we hear first.

What is a forward-deployed engineer?

An engineer who works inside your team on your real systems, rather than advising from outside. They own the build until it runs in production, then hand it to your people.

How is this different from hiring consultants?

You get engineers in your repo from week one, not a strategy deck in month three. The scope is one workflow, the outcome is a system in production measured on your numbers, and one team is accountable for it.

How long does a build take?

Six weeks for one workflow: a week to pick and scope it, four weeks to build, harden and run it in production, and a final week to hand it over.

What do you need from us?

One workflow that costs real hours, a person who owns it on your side, access to the systems it touches, and a few people who will use it every day while we build.

Which models and tools do you use?

Whatever fits your stack and your data rules. We build in your cloud and your repos, so you're not locked into anything of ours.

What happens after week six?

Your team owns the code, the evals and the documentation. Many teams pick the next workflow from the roadmap we leave behind.

Bring one workflow that costs real hours.

A 30-minute call. We'll tell you honestly whether it can be in production in six weeks, and we say no more often than yes.

Or write totalk@buildfastwithai.com