What we build
What this service covers, in detail.
AI coding assistants, workflow builders, research agents and automation with human approval.
- 01
AI coding assistants
Assistants that understand your repository conventions, suggest changes and run checks before proposing them.
- 02
AI research agents
Agents that gather sources, extract facts and compile cited briefs for analysts.
- 03
Business automation agents
Agents that handle intake, triage, reconciliation and reporting steps inside defined permissions.
- 04
Automated testing
AI-assisted test generation and maintenance, reviewed by engineers before merging.
- 05
AI code review
Review bots that flag risks, missing tests and style issues on every pull request.
- 06
Deployment troubleshooting
Assistants that read logs and configuration to explain a failed release and propose a fix.
- 07
Workflow builders
Visual canvases where non-engineers compose triggers, agents, conditions and actions.
- 08
Agent orchestration
Coordination, retries, budgets and timeouts across several cooperating agents.
- 09
Human approval systems
Approval queues, thresholds and audit trails for actions with real-world consequences.
- 10
Agent evaluation and monitoring
Scenario tests, scoring and tracing so agent behaviour is measurable and improvable.
- 11
Voice and multimodal agents
Phone and voice assistants and agents that read images and documents as part of a workflow.
Example use cases
Where teams put this to work.
- 01
Support triage
Classify incoming requests, draft replies and escalate sensitive cases to a person.
- 02
Engineering productivity
Review bots and assistants that shorten feedback loops for developers.
- 03
Back-office automation
Invoice intake, matching and exception handling with approval thresholds.
- 04
Phone appointment booking
A voice agent that books, moves and confirms appointments using your calendar.
Technical approach
Tools we typically use here.
A starting point, not a mandate. We adapt to your existing stack and constraints.
- Python
- TypeScript
- LangGraph
- MCP
- FastAPI
- PostgreSQL
- Queues
- Speech-to-text and TTS providers
- GitHub APIs
How it works
The engagement, step by step.
- 1
Pick the workflow
Choose a repetitive, well-bounded process with clear success measures.
- 2
Define permissions
Decide what the agent may do alone and what needs approval.
- 3
Build and evaluate
Prototype with scenario tests and measure against a baseline.
- 4
Pilot with a team
Run alongside people, collect feedback and tighten behaviour.
- 5
Roll out and monitor
Expand scope gradually while tracking quality and cost.
FAQ
Frequently asked questions
Will agents act without anyone checking?
Only for actions you explicitly allow. Sensitive or irreversible steps go through approval.
How do you measure whether automation works?
Against a baseline of the manual process, using accuracy, time saved and error rates on real cases.
Can we use our own models?
Yes. Provider-independent design lets you use hosted or self-hosted models as your policies require.
Projects
Reference projects that show this in practice.
Systems we designed as demonstrations, using sample data. Each has features, architecture and security notes.
Related capabilities
Often combined with
AI Engineering
Agentic systems, LLM-powered applications and enterprise assistants engineered with evaluation, security and oversight.
RAG & Enterprise Search
Permission-aware retrieval, citation-based answers and hybrid search across your organisation's knowledge.
API & Integration Engineering
Well-designed APIs, third-party integrations, webhooks, MCP servers and developer portals.
Planning a AI Automation & Developer Tools project?
Tell us the problem you are solving. We will reply with questions, options and a realistic next step.