AI Applications & Knowledge

Custom AI Development

Most AI projects fail as software projects, not as AI projects: unclear ownership, no definition of correct, no plan for the day the model is wrong. We build AI applications the way we build any production system, with the model as one component among many rather than the whole architecture.

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The business problem

The prototype was the easy ninety percent

A working demo takes days. What takes months is everything the demo skipped: authentication, tenancy, what happens under load, what happens when the provider has an outage, how cost scales with usage, how you find out the quality dropped, and who is accountable when it produces something wrong in front of a customer. Teams routinely discover this after the demo has been shown to an executive and a date has been committed.

What we do

Start from the process, not the model

We begin with what the software has to accomplish and who depends on it, then design backwards to the model. That produces boring, useful decisions: where a deterministic rule beats a model, where a cheaper model is sufficient, where the answer needs a human before it counts. We build with your identity provider, your data boundaries and your deployment pipeline, and we leave tests, documentation and an evaluation suite behind so the system survives our departure.

Custom AI Development

Capabilities

Custom AI Applications
GenAI Applications
Internal AI Tools
AI Platforms
AI Product Engineering
Production AI Systems

Common use cases

Build an internal tool that removes a manual step costing a team several days a month.
Add an AI capability to a product you already sell, with tenant isolation and per-customer cost control.
Replace a brittle rules engine that breaks whenever a form or a supplier changes.
Take a proven prototype and make it something operations will accept responsibility for.

How we deliver

Shape

Turn the request into a specification: who uses it, what a correct answer is, who decides.
Ground

Connect to the content and systems that hold the answers, respecting existing permissions.
Evaluate

Score against a labelled set built from your own cases, before anyone outside sees it.
Ship & operate

Staged release with monitoring, cost control and a regression suite that guards quality.

Technology

OpenAI
Anthropic
Azure OpenAI
pgvector
Elasticsearch
Microsoft 365
SharePoint
Confluence
Salesforce

Security & governance

Answers are grounded in your own content and carry citations, so a reader can check them. Retrieval respects the permissions already set on the source, which means a user never sees through the application what they could not see directly. Prompts, retrieved context and responses are logged for audit, and evaluation runs continuously rather than once at launch.

Engagement models

AI Project

We take responsibility for designing and delivering a defined AI solution.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

We are engineers who use AI, not the other way round

The interesting problems in an AI application are the ordinary ones: state, permissions, failure handling, cost, observability and change over time. We have been solving those for other people's production systems for years. That is also why we will tell you when a model is the wrong tool, which is more often than the market currently admits.

Selected clients

Frequently asked questions

How do you scope something nobody has built before?

We scope the first increment rather than the whole thing: one process, one user group, a bounded prototype measured against your own cases. That produces evidence about feasibility and cost before anyone commits to the full build, and it is usually four to eight weeks.

Who owns the code?

You do. It lives in your repositories, deploys through your pipeline, and runs on your infrastructure. There is no runtime dependency on us and no licence to keep paying.

What if a cheaper model would do?

We will say so. Model choice is a cost and latency decision as much as a quality one, and we design for provider portability so it stays reversible. Many production paths use a smaller model for most traffic and escalate only the hard cases.

How do you handle the model being wrong?

By deciding in advance what wrong looks like and what happens then: confidence thresholds, human review on consequential outputs, fallbacks to a deterministic path, and logging that makes an error reconstructable afterwards. A system with no defined failure behaviour is not finished.

Can you work alongside our own engineers?

Yes, and it is usually the better outcome. Building with your team rather than for them means the knowledge stays after the engagement ends.

Related capabilities

Agentic AI & Automation

AI Agents

Software that completes multi-step work inside your systems, under your access controls and your approval rules.

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AI Applications & Knowledge

AI Integration

Connect AI to the ERP, CRM and data systems where the work and the records actually live.

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AI Applications & Knowledge

RAG Development

Retrieval systems that ground answers in your own content, with citations and measurable accuracy.

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Discuss Your AI Initiative

Production AI applications built for your business, not configured from a template.

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