AI & Automation
AI that performs work, not just conversation.
Agents, intelligent workflows, and document processing wired into the systems your business already runs on.
The problem
Where AI earns its place
A chat window bolted onto a website rarely changes how a business performs. The value shows up when a system can read the information your team reads, make the same routine judgement calls, and then take the next action inside your actual tools.
Staff spend hours reading, sorting, and re-keying information between systems.
Support agents answer the same questions repeatedly from scattered documentation.
Invoices, forms, and contracts arrive as PDFs and get processed by hand.
Work stalls between steps because nothing moves until a human notices it.
Scope
What we can build
AI agents for multi-step workflows
Systems that carry a task through several steps — retrieving context, deciding, acting in connected tools, and escalating to a person when confidence is low.
Document processing pipelines
Extract, classify, and summarise information from invoices, forms, contracts and reports, with structured output your systems can use.
Knowledge-grounded support
Support experiences answering from your own documentation and product knowledge, with clear handover to a human when needed.
Intelligent internal tools
Drafting, summarising, classifying and triaging built directly into the software your team already uses each day.
AI features inside your product
Add intelligent capabilities to an existing application without rebuilding it around a model.
Capabilities
Key capabilities
- Retrieval over your own documents and data
- Tool and API access for agents
- Structured, validated model output
- Human-in-the-loop review and approval
- Confidence thresholds and escalation rules
- Evaluation and regression testing of prompts
- Cost and token monitoring
- Access control over sensitive data
Stack
Technology
Models
- LLM APIs
- Embeddings
- Structured output
Orchestration
- AI agents
- Tool calling
- Workflow engines
Data
- PostgreSQL
- Vector search
- Document stores
Integration
- REST APIs
- Webhooks
- Queues
Process
How we approach an AI build
Find the real bottleneck
We look for the task that is high-volume, rule-heavy, and currently manual. That is where automation pays for itself; novelty features rarely do.
Prove it on your data
Before committing to a build, we test the approach against your actual documents and cases to see how it behaves on the messy examples.
Design the safety rails
We decide what the system may do on its own, what needs human approval, and what happens when it is uncertain — before it touches production.
Integrate and measure
The system is wired into your tools and instrumented, so accuracy, cost, and time saved are visible rather than assumed.
Tune with real usage
Models and prompts get refined against the cases the system gets wrong once real work starts flowing through it.
FAQs
Common questions
Other services
- Custom SoftwareBusiness applications, dashboards, portals, internal tools, and platforms designed specifically around your workflows.Learn more
- SaaS DevelopmentTurn your idea into a scalable SaaS product — from MVP to production.Learn more
- E-CommerceModern, high-performance online stores, payment integrations, inventory systems, and commerce platforms.Learn more
What would you automate first?
Tell us about the task eating the most hours. We'll give you a straight answer on whether AI is the right tool for it.