AI Agents & Automation
Workflows that run themselves.
Agents handle support, operations and document workflows end to end, with people approving exceptions.
Support triageDocument processingOps workflowsHuman review
Put AI to work where it pays off.
We find the use cases worth funding, build on your own data, and run AI in production alongside the systems you already have.
The problem
- 01
Pilots that never reach production.
Teams start with a model instead of a measurable outcome, so projects stall at the budget review.
- 02
Data that isn’t ready.
Knowledge sits across systems, formats and permissions that AI can’t reach safely.
- 03
AI with nowhere to plug in.
If it can’t act in your ERP, CRM or ticketing tools, it stays a demo.
What’s included
Four ways to put AI to work.
Start with one, or combine them into a single program run by one senior team.
AI Strategy & Readiness
Know where AI pays off first.
Use-case discovery, data audits and an adoption roadmap ranked by return.
Use-case discoveryData auditRoadmap
Custom AI Applications
Answers grounded in your own knowledge.
LLM apps, chatbots and copilots that draw on your documents, policies and data.
CopilotsChatbotsRAG search
AI Integration
AI inside the systems you already run.
AI features embedded into your products, ERP, CRM and ticketing tools.
ERP & CRMProduct featuresGuardrails
Example workflow
A lead-qualification agent, step by step.
Illustrative example, not a client result.
01
Inquiry arrives
From a web form, email or event list
02
AI researches the company
Industry, size, location and what they’re asking for
03
Scored against your ideal customer
Fit and urgency, with the reasons shown
Human in the loop
04
A rep reviews qualified leads
Only good-fit leads reach sales, each with a drafted first reply
05
Logged to your CRM
Contact, notes and next step created automatically
Use cases
What this looks like in practice.
Illustrative examples of the work, not client results.
Example
Document intelligence
Contracts and reports summarized and searchable, with key terms pulled out for review.
Example
Support triage
Tickets classified, routed and drafted in seconds, ready for an agent to review.
Example
Knowledge assistant
Answers grounded in your policies and documents, with sources your team can check.
Who it’s for
Built for established teams.
- 01
Operations leaders
with high-volume, manual workflows in support, finance or back office.
- 02
Product leaders
adding AI features to an existing product without a rebuild.
- 03
CIOs and CTOs
who need security, governance and integration before anything ships.
How an engagement works
From first use case to production.
01
Assess
We map use cases, data and systems, and agree on the metrics that define success.
02
Prototype
A working prototype on your real data, tested with the people who will use it.
03
Productionize
Security, evaluation, monitoring and integration so it holds up in production.
04
Scale
Roll out to more teams and workflows, and keep improving what’s live.
Security & governance
Built in from day one.
Your data stays in your environment
We deploy inside your cloud or approved providers.
No training on your data
Your data is never used to train third-party models.
People in the loop
High-risk steps require human approval.
Evaluated and monitored
Quality is measured before launch and tracked after.
You own what we build
Code, prompts and configurations built for you are yours.
FAQ
Questions, answered.
We are model-agnostic. We choose between leading commercial and open-source models based on accuracy, cost and where your data needs to stay.
Yes. We work inside your environment or approved providers, never train third-party models on your data, and follow your security and access policies.
We agree the scope and timeline up front, and aim to put a working prototype on your real data in front of users early.
You do. Code, prompts, configurations and documentation built for you belong to you.
Yes. Integration is core to how we work, including ERP, CRM, ticketing, document stores and internal APIs.