AI built to do useful work, not just look impressive.
We build AI tools around real tasks, clear limits and human review, so your team can actually use them in everyday work.
Assistants, search, workflow automation and analysis
ai.pipeline
Stage 01 - Ingest
Questions, documents and events enter the system
Requests routed
3,842
91% avg confidence
Where AI can save real time.
Not every task needs AI. These are the areas where it can save real time. Pick one to see how it works in practice.
usecase.sim / assistants
Focused assistants that help customers or internal teams answer questions, prepare work, and complete defined tasks using the right context.
Input signalcapturing
Output - ready for review
A cited answer drafted from your help content, ready for review.
See how the system reaches an answer.
A good AI system is not a mystery box. Watch how a request moves through retrieval, reasoning and review before anything reaches a user.
Ask a question and see the source.
This demo searches a knowledge base and answers with citations. Every answer should come with clear evidence.
Try typing - or watch it search on its own.
Cited answer
Annual plans can be refunded within the first 30 days1. After that, refunds follow the approval rules in the sales playbook3.
For anything unusual, the answer says so instead of guessing, and hands the conversation to your team.
You decide what the AI can and cannot do.
Approval rules, data boundaries, escalation paths. Toggle the controls and see how the system's behaviour changes. Your team sets these, not the model.
Control panel - illustrative
2 of 4 guardrails on
- Approved sources only
Privacy by design
Decide what information the system may use, where it is processed, how long it is retained, and who can access the result.
- Review before sending
Human approval
Add review steps where an incorrect answer could affect a customer, payment, legal process, or important business decision.
- No refusal rules
Clear boundaries
Define what the assistant should handle, what it should refuse, and when it should hand the conversation to a person.
- No review loop
Evaluation and monitoring
Test with realistic examples, watch recurring failure patterns, and improve the system based on evidence rather than impressions.
Review the results and keep improving them.
AI quality is not a launch-day promise. It is a loop of testing, feedback and refinement that keeps working after we hand over the keys.
- 01
Choose the task
We narrow the idea to a clear user, input, expected result, and reason the feature should exist.
Sample run
Rough idea
A clear task statement
- 02
Test the approach
We create a focused prototype using realistic examples to learn where the approach works and where it struggles.
Sample run
First prototype
Known strengths and gaps
- 03
Build the product experience
We design the interface, context retrieval, model behavior, safeguards, feedback, and human review as one connected system.
Sample run
Working feature
Guarded and reviewable
- 04
Measure and improve
We evaluate output quality, speed, cost, and user behavior, then improve the parts that make a meaningful difference.
Sample run
Measured system
Improving on evidence
Is repetitive work eating up your team's time?
Tell us what is slowing the team down. We will tell you whether AI can help, what it would take and what should stay with a person.
Talk about an AI toolRelated services
Prefer the wider view? See the full development overview.