AI Agents & Business Automation
AI agents that work inside clear boundaries
We design, build and operate AI agents and features that connect to the systems you already run, such as your CRM, ERP, help desk and documents. Each one has defined permissions and knowledge boundaries, and a person approves anything with real stakes.
What you get
- Value you can measure. Each agent is scoped to a specific process with agreed measures, and someone owns the result.
- Control stays with you. Clear permissions and approval points keep consequential decisions with your people.
- Supported after launch. Evaluation, failure review and controlled updates continue once the agent is live.
(01) Overview
Part of our Software & App Development capability.
Useful AI in a business is rarely a chatbot on its own. It's an agent that reads the right documents, looks up the right records, drafts the reply or updates the system, and knows when to stop and ask a person.
We start with the process and the risk, then decide what the agent may see, what it may do and where a person approves the result. Then we engineer it like any other production system. It is grounded in your approved knowledge, connected through scoped integrations, evaluated against real cases before launch and monitored after it. When a model, prompt or tool changes, the update goes through the same testing and release controls as code. And because AI-assisted delivery and AI features are different things, we only recommend an AI feature where it solves a real problem.
Is this the right service?
Choose AI agents & automation when
- A repetitive, text- or data-heavy process could be handled by an agent with clear limits
- You want answers grounded in your own documents and records, with a person approving what matters
- An AI feature in your product needs evaluation and ongoing operation, not just a demo
Consider instead
- Software & App Development if you need a whole product or app built, with AI used to build it rather than inside it.
- Enterprise Application Integration if the systems just need to share data; no judgment is involved.
(02)Deliverables
What's included in AI agents & automation.
- 01
Use-case discovery
Find the processes where an agent saves real time, and size the value and the risk before anything is built.
- 02
Knowledge assistants
Answers grounded in your approved documents and data, with citations and clear limits on what the assistant knows.
- 03
Agents connected to your systems
Agents that look up, draft and update records in your CRM, ERP or help desk through scoped, auditable integrations.
- 04
Document & data automation
Extraction, classification and routing for emails, forms, invoices and tickets, with confidence thresholds and human review.
- 05
AI features in your product
Search, recommendations, classification and vision features built into your own product and evaluated on your data.
- 06
Evaluation & operations
Test sets, quality monitoring, failure review and controlled updates after launch.
(03)AI & responsibility
How AI helps us build agents.
Where AI helps
- Drafting prompts, tool definitions and evaluation cases from your real examples
- Generating test conversations and edge cases
- Summarizing failure reviews to spot patterns
- Implementing integrations under engineer review
What our experts own
- What the agent may read, do and escalate
- Approval points and fallbacks
- Evaluation criteria, agreed with you
- Releases of every model, prompt or tool change
(04)Control by design
What an agent may see, do and hand back to a person.
- 01
Permissions
Each agent gets the narrowest access that does the job, with its own credentials and an audit trail.
- 02
Knowledge boundaries
Sources are defined and curated, and the agent declines questions outside them instead of guessing.
- 03
Approval points
Actions with financial, legal or customer impact wait for a person to approve them.
- 04
Failure handling
Timeouts, tool errors and low-confidence answers go to a person, with the context attached.
(05)Our process
How we deliver AI agents & automation.
- 01
Map the process
Understand the workflow, the data, the people involved and what a mistake would cost.
- 02
Define boundaries
Agree what the agent may read, which actions it may take and where approval is required.
- 03
Prototype on real cases
Build a working prototype and test it against examples from your own operation.
- 04
Evaluate & harden
Measure quality, failure modes and cost, then add guardrails, integrations and monitoring.
- 05
Launch & operate
Release in stages, review failures, and update models, prompts and tools under change control.
(06)Connected capabilities
Connected work, one accountable team.
How the other capabilities support AI agents & automation on a project.
QA & Release Assurance
AI-product evaluation covers output quality, tool permissions, unsafe actions and failure handling before launch and after each change.
DevOps & Managed Operations
Inference latency and cost, quality trends and escalations are monitored, and updates are released under change control.
Product Design & UI/UX
Interfaces show sources, confidence and the moment a person takes over, so users know when to trust the answer.
How we build with AI
Choose how AI is used while we build.
Agents can run on cloud models or privately hosted ones, and the same choice applies to how we build them. Pick the approach that fits your data and processing requirements.
Private / Local AI Engineering
AI-powered software delivery in a controlled processing environment.
Claude Code & Codex Engineering
AI-accelerated delivery with Claude Code and OpenAI Codex, directed by experienced engineers.
(07) Tools & technologies
- OpenAI / Anthropic / Gemini APIs
- Open-weight models
- RAG & vector databases
- Python
- TypeScript
- LangChain / LlamaIndex
- Evaluation suites
- PyTorch
- OpenCV
(08)Industries
Where this matters most.
(09)FAQ
AI Agents & Automation: your questions.
Where should we start with AI agents?
With one process that is repetitive, text- or data-heavy and easy to measure, such as triaging support requests, processing documents or preparing quotes. A short discovery sprint identifies two or three candidates and estimates their value and risk before you commit to a build.
Can an agent take actions in our systems?
Yes, within limits we agree with you. Agents get scoped credentials for specific actions, anything with financial, legal or customer impact can require a person's approval first, and every action is logged.
Will our data be used to train public AI models?
Not by design. We choose providers, account types and settings whose terms fit your data, and confirm them before your data is processed; business offerings generally differ from consumer ones. Where your requirements rule out third-party processing, we can use privately hosted models instead.
How do you stop an agent from making things up?
By grounding answers in approved sources, testing against evaluation sets built from real cases, requiring citations where it matters, and routing low-confidence or out-of-scope questions to a person. No method removes errors completely, which is why approval points and monitoring stay in place after launch.
Do privacy laws affect AI features?
Yes. PIPEDA's consent and purpose principles apply to data used by AI, and Quebec's Law 25 requires informing people when a decision about them is made exclusively by automated processing. We build in the transparency, consent and logging needed to meet those obligations.
Is this the same as using AI to build our software?
No. We use AI agents to engineer every product (see AI Engineering & Automation). This service is about AI that your customers or staff use, and many products don't need it.
Other services
Capabilities
How we build with AI
Next step
Let's talk about your AI agents & automation project.
Tell us what you're building. We'll come back with practical next steps, a realistic plan and an honest estimate.