At a glance

Custom AI development, built for production from day one

Custom AI development company

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Free 30-min call. No pitch, no slides.

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

What custom AI development means here

What it is

Off-the-shelf tools cover the common cases. Custom development is for the rest: an agent that has to work across your specific systems, an AI feature inside a live product, or a tool shaped around how your team works.

We build in a fixed order. Define the job and how success is measured. Collect real examples to test against. Build the smallest version that does the job. Test it against the examples. Release with logging and alerts. The test set is what tells us when the model, the prompt or the data needs fixing.

We work in TypeScript and Python with Claude and OpenAI models, chosen per task. For existing products, we ship inside the live product instead of proposing a rebuild.

Is this the right fit?

Fit check

Where this wins

Products that need AI features without a rebuild

Personalization, segmentation, matching or prediction shipped inside the live product.

Teams with unique data and workflows

Agents and tools built around your systems, permissions and language needs.

Founders launching an AI-native product

Architecture through to a working product, built to be extended.

Products that need AI features without a rebuild Personalization, segmentation, matching or prediction shipped inside the live product.
Teams with unique data and workflows Agents and tools built around your systems, permissions and language needs.
Founders launching an AI-native product Architecture through to a working product, built to be extended.

When NOT to use this

An off-the-shelf tool already does the job. We will say so, and the build vs buy page helps you check.
You want a research lab. We build applied systems for defined jobs.
You cannot provide examples or data access. Without something to test against, we cannot show it works.

How a custom build runs

How it works

Small, tested steps, so you see working software early and the risk shows up before the budget is spent.

Typical timeline

A focused build, scoped after the discovery call

Stack we build with

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

// 01

Define the job

Write down what the system must do, for whom and how we will know it works.

// 02

Collect examples

Gather real inputs and the outputs a good result should produce.

// 03

Build the first version

The smallest version that does the job, connected to your systems in a sandbox.

// 04

Test and tighten

Run the test set, fix what fails and add approvals where errors are costly.

// 05

Release and monitor

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

01 Define the job Week 1 Write down what the system must do, for whom and how we will know it works. Deliverable One-page spec and success measures
02 Collect examples Week 1 to 2 Gather real inputs and the outputs a good result should produce. Deliverable Test set
03 Build the first version Week 2 to 5 The smallest version that does the job, connected to your systems in a sandbox. Deliverable Working first version
04 Test and tighten Week 5 to 7 Run the test set, fix what fails and add approvals where errors are costly. Deliverable Test report
05 Release and monitor Go live with logging and alerts, then review the first weeks of real use. Deliverable Live system and monitoring

Custom build or off-the-shelf?

We tell you straight if a tool you can buy does the job. If not, we scope the smallest custom build that proves the value.

What we build most often

Use cases

Customer Support Automation

Marketing Operations Automation

Legal & Compliance Automation

Saved €10K in Fines

AI features in a live product

Personalization, segmentation and churn prediction inside an existing platform.

Multilingual document and language tools

Systems that read and summarise documents in the languages your users work in.

Matching and recommendation engines

Automatic matching between people, roles, products or opportunities.

What your first 30 days look like

First 30 days

A spec, a test set and a first version running in a sandbox.

Spec and success measures

Week 1

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

Test set

Week 2

Real examples collected, with the outputs a good result would produce.

First working version

Week 3 to 4

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

First test report

Day 30

Where it passes, where it fails and what we change next.

Scope and exclusions

Scope

In scope

Job definition and success measures
Agent, feature or tool build on your data and systems
Test set from real examples and regression checks
Logging, alerts and a monitoring plan
Handover and, if you want it, ongoing maintenance

Not in scope

Training foundation models from scratch
Hardware or on-device model work
Accuracy guarantees on data we have not seen
Products with no defined user or job

Results from custom builds

Results

Achieved results only. Clients that have not agreed to be named are described instead.

Two engineers shipped personalization, segmentation and churn prediction inside the live product and left an architecture that lets new AI features keep shipping without a rebuild.

A platform built from the ground up in under a month that reads and summarises documents in Arabic, French and Darija.

A full AI concierge product delivered from architecture through to a working product, signed and live.

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.

Scope your custom AI build

Next step

Book a free audit call. Bring the job, a few real examples and the systems involved.

Describe the job you want done

A 30 minute call. Tell us the job, the data and the systems, and we tell you what a custom build would involve.

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.

Consultation

This call is for teams ready to move. If that's you, pick a time.

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

At a glance questions

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

Do you build on our data without training a model on it?

We use retrieval, tools and prompts against your data through approved model routes. Training or fine-tuning is a separate decision and only makes sense when testing shows it helps.

How do you know the AI works?

We build a test set from real examples before the build and run it after every change. Results and failures go in the test report, so the decision to go live rests on evidence.

Can you add AI to a product we already run?

Yes. We ship inside the live product and leave an architecture that supports new AI features without a rebuild.

What does it cost?

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

Who owns the code?

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