For the model that still has to reach your systems

AI Integration Services

A model proven in isolation is worthless sitting outside the systems your team actually works in every day. This practice wires it in with a data contract, error handling and a fallback for the moment it goes quiet. That applies whether it is one CRM or an integration that keeps breaking on updates.

Talk to UsScope the Work
AI augmented, human governedEvidence before estimatesYour IP from day oneOne accountable owner per engagementFounded in 2011. Part of the Vinove Group500+ full-time engineers, as of September 2026NASSCOM member
Watch · the 90-second brief

How a model connection survives a provider update

A data contract, a tested fallback and the monitoring that catches a break early.

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A contract-tested AI integration surviving a model provider update.90 secSample frame · video pending

01Before any contract is written

The integration decision worth making explicit

Wiring a model into a live system is not a small technical detail. Three checks decide whether the integration is ready to start.

Is the model itself already proven?

Wiring an unproven model into a CRM only spreads a problem that has not been solved yet.

What happens if the model fails mid-workflow?

A fallback path has to exist before launch, or a model outage becomes a business outage by default.

Who owns the data contract between systems?

Every field exchanged needs an owner, or a schema change on either side breaks the integration silently.

02Agreed before any system is touched

The ledger that keeps integration honest

Every integration engagement names the split before a connection exists: the systems you bring, what gets engineered between them, what you receive, and what stays under your rules.

  1. Your side

    You bring

    • The model or AI service already chosen
    • Access to the CRM, ERP or internal system
    • The workflow the integration is meant to change
  2. Our side

    We build

    • An integration layer with a defined data contract
    • Fallback behaviour for when the model is unavailable
    • Error handling that does not corrupt existing records
  3. Your side

    You receive

    • A live integration inside the system your team uses
    • A documented contract for every field exchanged
    • A tested fallback path for model downtime

Never leaves you

  • The CRM or ERP and its data model
  • Business rules the integration must respect
  • Final approval before the integration goes live

Where the line sitsWe wire the model in safely. What the system does with the result stays governed by your existing rules.

03Three drivers, not a rate card

What actually drives integration cost

Integration work is priced against the assessment, not a template, because these three factors change the effort involved by multiples.

Driver 01

Number of systems being connected

One CRM integration is simpler than middleware serving several platforms with different data models and constraints.

Driver 02

Fallback and resilience requirements

A best-effort connection is faster to build than one engineered to survive every plausible model outage.

Driver 03

Data contract complexity

A handful of simple fields is quick to specify. A contract spanning many systems and edge cases takes materially longer.

04Evidence and proof

Integrations still standing after the model provider's next update.

Integration proof is a contract that survived a real change, not a connection that only worked on launch day. Every record here is a preview awaiting the client's written approval.

Explore case studies
A clinical extraction model wired into an existing patient record system.
Koru Health · Healthcare, US

A clinical model finally reached the record system clinicians actually use.

Koru Health had a working extraction model sitting outside their patient record system. A contract-tested integration put its output where clinicians could see it.

Manual re-entry avoided
Across every processed record
Integration outages since launch
Zero
Sample · pending client approval
A contract-tested integration absorbing a model provider's update.
Nexa Foundry · Industrial manufacturing, EU

A brittle model connection stopped breaking on every vendor update.

Nexa Foundry's integration failed every time the model provider shipped an update. A rebuilt, contract-tested connection absorbed the next update without an incident.

Incidents from provider updates
Reduced to zero
Fallback coverage
Every downstream system
Sample · pending client approval
Portrait of a technology executive reviewing an integration monitoring view.

The integration kept working through two model provider updates we did not plan for. That is the part nobody warns you needs engineering.

Daniel MercerCTO, Vela Ledger
Sample · pending client approval

05Read this first

When integration work is the wrong starting point

Selling a one-time integration to a model with no owner watching it afterwards would cost more than it earns us in trust. Alternative starting points are marked before any pitch.

  • The model is already live and needs an owner

    Ongoing operation, drift monitoring and cost management belong to managed AI services, not a one-time integration build.

    Managed AI Services

  • No model exists yet to integrate

    Building the model itself, rather than wiring one in, belongs to machine learning development instead.

    Machine Learning Development

  • The use case itself is still unproven

    Rank and test it first, so the integration gets built around a case that has already earned it.

    AI Consulting
Engagement shapes for AI integration services
ShapeBest whenYou directWe carry
Scoped buildThe systems and contract are settledPriorities and sign-offContract design, engineering, testing
Delivery podIntegrations expand as more systems connectProduct decisions each cycleEngineering, testing, releases
Team extensionYour leads already run integration work wellArchitecture and standardsNamed engineers and continuity

06Straight answers

Asked before a model gets wired anywhere.

Not answered here?

Ask us directly Or scope the work first
How is integration different from managed AI services?

Integration wires a model into your existing systems once, with a tested contract and fallback. Managed AI services then operate that connection over time: watching cost, drift and quality. Many clients start with integration and add managed operation once something is live.

Do you build the model, or just connect it?

This practice connects an existing model, whether hosted, open-source or already trained by another team. Building the model itself, when none exists yet, is a separate practice with a different scope and skill set.

What happens if the model provider changes their API?

The integration is contract-tested against version changes, so most updates get absorbed automatically. Where a change breaks the contract, monitoring catches it immediately rather than after a downstream system starts failing.

Can you integrate a model into a legacy CRM or ERP?

Yes, and it is one of the more common requests. The integration is engineered to respect the legacy system's data model and business rules, rather than asking you to replace either.

What does an integration project cost?

Scoped work is priced against the number of systems, fallback requirements and contract complexity, from the assessment. Ongoing delivery is billed monthly against a scope that moves with each new system connected.

How do you prevent one integration from breaking others?

Through a documented, owned data contract for every field exchanged. A schema change on either side is caught against that contract before it reaches a system nobody expected to be affected.

Do you support multiple models behind one integration layer?

Yes, through a middleware layer with one consistent contract, shared fallback behaviour and a single view of cost and latency across every model it serves.