AI ServicesCopilots & Internal
Copilots & Internal
Tools
Every organisation has knowledge trapped in places that are hard to reach. People waste hours searching, asking colleagues, or recreating work that already exists somewhere.
Engagement
Product & Workflow Design
Typical Duration
6 – 12 weeks
Purpose-built AI interfaces that make internal knowledge accessible and actionable. Not generic chatbots plugged into Slack. Tools designed around specific team workflows, connected to real data sources, with outputs structured for practical use.
What we build
Knowledge Copilots
Conversational search across docs, wikis, file storage. Users ask questions, get answers with source citations. Not search results, actual answers.
Data & Reporting Assistants
Non-technical users query business data through natural language. "What was revenue by product line last quarter?" gets a formatted answer, not a SQL lesson.
Drafting Copilots
AI pulls relevant context from past work, applies templates and standards, produces structured first drafts. Proposals, documentation, reports, content.
Workflow Assistants
Multi-step process guidance. Onboarding, deal desk, incident response. Pulling relevant data, automating routine parts, guiding decisions.
Code Copilots
AI-powered code review, documentation generation, test generation, PR summaries. Custom to the team’s patterns, standards, and libraries.
How we build
Discovery — Step 1
Understand the team, their workflows, their pain points, and their data landscape.
01
Discovery — Step 1
Understand the team, their workflows, their pain points, and their data landscape.
02
Design — Step 2
Define capabilities, data connections, interface, and success metrics.
Design — Step 2
Define capabilities, data connections, interface, and success metrics.
Build — Step 3
Core retrieval and generation pipeline first, then interface, integrations, and access controls.
03
Build — Step 3
Core retrieval and generation pipeline first, then interface, integrations, and access controls.
04
Evaluate — Step 4
Systematic quality testing against real use cases. Accuracy, relevance, completeness.
Evaluate — Step 4
Systematic quality testing against real use cases. Accuracy, relevance, completeness.
Deploy & Iterate — Step 5
Launch with monitoring, gather feedback, continuously improve.
05
Deploy & Iterate — Step 5
Launch with monitoring, gather feedback, continuously improve.
Deliverables
What you get
- Deployed tool connected to your data sources
- Full source code and documentation
- Quality evaluation results and benchmarks
- Monitoring dashboard for usage and quality
- Improvement playbook for ongoing refinement
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Team spending more time finding information than acting on it?
The difference between a demo-worthy chatbot and a tool people actually use every day is retrieval quality, prompt engineering, access controls, and error handling. That’s where we focus.