Agentic AI & Automation

AI Automation

Automation stalls at the exceptions. We automate the cross-system work that defeated rule-based tools, because the variation that broke them is exactly what a model handles well, with the deterministic parts left deterministic.

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The business problem

The exception queue is where the staff went

A rules engine handles the cases somebody anticipated. Everything else queues, and clearing that queue quietly becomes a job. Because the exceptions are individually rare and collectively constant, they never justify their own automation project, so the queue is permanent. Meanwhile every format change, new supplier or policy update adds to it.

What we do

Split the judgement from the mechanics

We separate the parts of a process that need interpretation from the parts that must be exact, and use the right tool for each. Reading an unstructured document, reconciling inconsistent references and deciding which case this resembles go to a model. Calculating totals, applying entitlements and writing to a system of record stay deterministic. The result is automation that absorbs variation without becoming unpredictable where it matters.

AI Automation

Capabilities

Finance Automation
Procurement Automation
HR Automation
Operations Automation
Compliance Automation
Back-Office Automation

Common use cases

Clear an invoice or claims exception queue that has been permanently staffed for years.
Automate a process that spans several systems with no common identifier.
Replace a rules engine that requires a change request for every new supplier format.
Handle onboarding steps where the documents arrive in whatever form the customer sends.

How we deliver

Process discovery

Map the current path, its branches, its exceptions and its definition of correct.
Bounded prototype

One process, read-only first, measured against a labelled set before it writes anything.
Guardrails & approval

Permissions, rate limits, approval gates and audit logging before production access.
Production & operate

Staged rollout with continuous evaluation, cost monitoring and a rollback path.

Technology

OpenAI
Anthropic
Google Vertex AI
Azure OpenAI
AWS Bedrock
Model Context Protocol
LangGraph
Temporal
OpenTelemetry

Security & governance

Software that acts inside your systems is a privileged identity and is treated as one. It authenticates through your identity provider, holds least-privilege scopes, and cannot exceed the permissions of the user on whose behalf it acts. Tool calls are validated and rate-limited, untrusted content is treated as data rather than instruction to limit prompt injection, and consequential actions require human approval. Every run is logged with inputs, calls and outputs, which is what makes the system auditable.

Engagement models

AI Project

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

Forward-Deployed AI Team

A cross-functional AI team works inside your organization to continuously find and deliver opportunities.

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 keep the deterministic parts deterministic

The failure mode in AI automation is letting a model do arithmetic or apply entitlements because it can appear to. We are explicit about which parts must be exact and build those as code, which is why the outputs reconcile and the process survives audit.

Selected clients

Frequently asked questions

Will this replace our existing automation?

Usually not. It handles what your existing automation routes to people. The rules that work keep working, and we add the layer that absorbs the variation they cannot.

What accuracy is achievable?

It depends on the process, so we measure on your cases before committing to a number. More useful is the confidence threshold: raise it and more goes to review with fewer errors through, and we tune that against your cost of each.

Do people still review anything?

Yes, by design. Low-confidence cases and anything consequential go to a person, with the source and the proposed action side by side. The aim is to shrink the review queue, not to abolish oversight.

How long until something is running?

A single process, read-only first and measured, is typically six to eight weeks before it writes anything. Subsequent processes are faster because the integration and review patterns already exist.

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

Document Intelligence

Extract, classify and route the documents that currently move through people's inboxes.

Learn more

AI Applications & Knowledge

AI Integration

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

Learn more

Discuss Your AI Initiative

Automate the cross-system processes that rule engines could never handle reliably.

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