Grounded answers
Retrieval, source context and permissions reduce the gap between a confident answer and a useful one.
08 / AI & integrations▮
AI & integrations
AI with a job, permissions and an invoice trail.
AI features connected to real data, permissions and workflows: assistants, RAG, extraction, classification and agentic steps where they create measurable leverage—not a chat box added because the board asked about AI.
Build what's next
THE OPERATING IDEA
If ChatGPT in another tab solves the problem, we will tell you.
Useful AI needs context, guardrails, observability and a reason to exist inside the product. We design the job first, then decide which model deserves it.
AI with a measurable job description.
Controlled actions
Tool use, approvals and validation define what the model may do—not just what it can imagine doing.
Visible economics
Latency, success rate and cost per task are tracked so the feature can be tuned like software, not defended like magic.
LatencyCost / taskSuccess rate
Start with the task, not the model logo.
Define success
We pick a task with observable quality and decide what a good result means before writing prompts.
Build the evaluation loop
Real examples, edge cases, retrieval and tool behaviour are tested while the feature is still cheap to change.
Ship with guardrails
Usage, errors, latency, cost and human escalation are visible from day one.
QUESTIONS
Separate useful AI from expensive autocomplete.
We like models. We like measurable outcomes slightly more.
Which model do you use?+
Whichever best fits quality, latency, privacy and cost for the task. The architecture should not collapse because a leaderboard changed.
Can it use our private data?+
Yes, with an explicit data and permission model. We do not treat “connected to the knowledge base” as a security design.