At a glance

AI integration that writes to your systems of record without breaking them

AI integration services

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

What AI integration services include

What it is

An integration has three jobs. Get the right data to the model at the right moment. Take the model's output and turn it into a valid change in a system of record. And survive the ordinary failures: rate limits, timeouts, schema changes and duplicate events.

We use existing APIs, webhooks and exports where they exist. When an integration touches customer or financial records, writes go through validation and are logged, so a bad output produces a rejected write instead of a corrupt record.

We also put results where people already work. An AI feature inside the existing product gets used. One in a separate dashboard often does not.

Is this the right fit?

Fit check

Where this wins

Sales teams losing hours to research and CRM upkeep

Agents prepare accounts, update records and score leads before the call.

Existing products that need AI features

We add personalization, segmentation or matching inside the live product without a rebuild.

Teams with several disconnected systems

Inquiries, documents or records scattered across tools that need one tracked flow.

Sales teams losing hours to research and CRM upkeep Agents prepare accounts, update records and score leads before the call.
Existing products that need AI features We add personalization, segmentation or matching inside the live product without a rebuild.
Teams with several disconnected systems Inquiries, documents or records scattered across tools that need one tracked flow.

When NOT to use this

The system you want to connect has no API, export or database access. We will tell you what a workaround costs before building.
You want one more standalone AI tool. Integration is about existing workflows, not new destinations.
Data cannot leave a boundary and no approved model route exists. We resolve that first.

How an integration project runs

How it works

Read access first, writes second, and nothing writes to production until it has run on samples.

Typical timeline

Two to six weeks per integration, scoped after the audit call

Stack we build with

n8n · TypeScript · Python · Claude · OpenAI · PostgreSQL · Supabase · Webhooks

Systems and access map

List each system, its API, who owns credentials and what the AI may read and write.

Read path

Pull the right data into the model with the right context, in a sandbox first.

Write path with validation

Turn outputs into valid changes with schema checks, approvals where needed and logging.

Failure testing

Simulate timeouts, duplicates and bad outputs and confirm the system recovers.

Go live and monitor

Release with alerting and a runbook, then review the first weeks of logs.

01 Systems and access map Week 1 List each system, its API, who owns credentials and what the AI may read and write. Deliverable Integration map and access plan
02 Read path Week 1 to 3 Pull the right data into the model with the right context, in a sandbox first. Deliverable Working read integration
03 Write path with validation Week 3 to 5 Turn outputs into valid changes with schema checks, approvals where needed and logging. Deliverable Validated write integration
04 Failure testing Week 5 to 6 Simulate timeouts, duplicates and bad outputs and confirm the system recovers. Deliverable Failure test results
05 Go live and monitor Release with alerting and a runbook, then review the first weeks of logs. Deliverable Live integration and monitoring

Integrations we build most

Use cases

Customer Support Automation

Marketing Operations Automation

Legal & Compliance Automation

Saved €10K in Fines

CRM enrichment and updates

Records updated and leads scored automatically from research and call data.

Support desk automation

Requests classified, answered or routed with context attached.

Inquiry to booking tracking

Multilingual inquiries scored and followed up, tracked in one place.

What you have after 30 days

First 30 days

A working read path and a validated write plan, tested on samples.

Integration map

Week 1

Every system, API and credential owner listed, with what the AI may read and write.

Read integration

Week 2 to 3

The right data reaching the model in a sandbox, with the context it needs.

Validated writes

Week 4

Outputs turned into schema-checked changes on sample data, with logging.

Failure test report

Day 30

Timeouts, duplicates and bad outputs simulated, with fixes noted.

Scope and exclusions

Scope

In scope

Connectors to CRM, ERP, support, database and internal APIs
Credential scoping, validation of every write and audit logging
Retries, idempotency and error alerts
AI steps for extraction, classification, drafting or matching

Not in scope

Replacing or migrating your CRM or ERP
Custom development inside vendor platforms that forbid it
Integrations with systems that offer no programmatic access
Data warehouse or BI programme build-outs

Results from comparable work

Results

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

Agents do pre-call research, update the CRM and score leads automatically. Research time per prospect dropped and the sales team's conversion rate went up.

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.

Inquiries from several countries and languages are now scored and followed up automatically in the right language, with the full journey from inquiry to treatment tracked in one place.

How this is priced

Pricing

We do not sell fixed packages sight unseen. The number depends on how many systems the work touches, and the audit call tells you that number before you commit to anything.

Single workflow

Low four figures

One focused workflow live in one to two weeks, with error handling, retries and monitoring built in from day one.

Full system

Scoped after an audit call

AI steps, monitoring and integrations across several systems, typically four to eight weeks. No template price, because scope drives the cost.

Embedded engineer

From $60,000/year

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

List the systems you want connected

A 30 minute call. We look at your systems and tell you what the integration touches, what can break and what it would take.

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.

Can AI write directly into our CRM or ERP?

Yes, through validated writes. Every change is checked against the schema and logged, and risky changes can wait for a person. A bad model output becomes a rejected write, not a corrupt record.

Do you use n8n or custom code?

Both. n8n covers many integrations quickly, see our n8n agency page. Custom code fits tight security or heavy logic. The choice depends on maintenance cost and your team.

What if an API changes or fails?

Retries, idempotency keys and alerts handle the ordinary failures. Schema changes show up as alerts, and a maintenance plan means someone fixes them.

What does it cost?

A single focused integration starts in the low four figures. Multi-system builds are scoped after an audit call.

Which models do you use?

Claude and OpenAI models mostly, chosen per task. Where data cannot leave a boundary, we design around the route your security team approves.