AI & Analytics Agents
Ask your data a question. Check the answer.
AI is fast at finding a number and poor at knowing whether it is the right one. We build agents on your governed metric definitions, so the answer arrives in seconds, shows the query behind it, and a person with context signs off anything that drives a decision.
Book a free discovery call Teams we have built data for
Snap FinanceEmbrokerCigna HKENT Credit UnionEnclave
The problem
01
The pilot gave wrong answers
The demo looked good until it gave the CFO a revenue figure that didn’t exist.
02
The data isn’t ready
Without clean models and shared definitions, each AI tool gives a different answer.
03
Security can’t approve it
Compliance won’t sign off on a system they can’t audit.
One platform for people and AI agents
Grounded, reviewed and auditable
Your warehouse and semantic layer become the source that both dashboards and agents read from. Analysts review high-stakes answers, and each response shows the query and definition behind it.
What you get
- Plain-English questions answered with defined metrics
- Written summaries on key dashboards
- Automatic tagging and enrichment of text data
- A log of every prompt, query and answer
- A readiness score and a roadmap for next steps
What we deliver
What the engagement includes
Each engagement has a fixed list of deliverables, agreed before work starts.
Conversational analytics
Questions in plain English, answered through your semantic layer.
Dashboard summaries
Daily and weekly write-ups of what changed and the likely cause.
Classification and enrichment
LLM pipelines that tag tickets, reviews, calls and leads at warehouse scale.
Governance
Access controls, logging, evaluation sets and human review where needed.
Our method
How the work runs
01
Semantic grounding
Agents only use defined metrics.
02
Analyst review
People check high-stakes answers.
03
Audit trail
Every answer links back to its source.
04
Readiness check
We assess goals, data, culture and skills.
Tools we work with
ClaudeGeminiOpenAIVertex AILangChainMCPCubeLookerdbtBigQuery
Is this for you?
This works best if
- Leadership wants AI on your data this year
- You have a warehouse or are ready to build one
- Accuracy matters more than a quick demo
What if our data isn’t ready?
That is often the first step. The readiness assessment lists what to fix, and we can fix it before or alongside the agent build.
Which model do you use?
The one that suits your cloud, security and cost needs, usually Claude, Gemini or OpenAI. The semantic layer makes it easy to switch later.
How we work
How an engagement starts
The work runs in three steps. You see working output within weeks and can stop after any stage.
01
Discovery call
A 30 minute call with the engineer who would do the work. We look at your setup, tell you what we would fix first, and whether you need us for it.
Day 1 02
Blueprint sprint
We map your sources, stakeholders and goals, then give you an architecture and a prioritised roadmap. You keep it whether or not we do the build.
2 to 4 weeks 03
Build in sprints
We ship working tracking, models, dashboards and agents every two weeks. You can stop after any sprint, and everything built stays yours.
Every 2 weeksWhat happens on the call
Book your call- 30 minutes with the engineer who would do the work
- No slides or sales pitch
- A clear answer on whether we can help, and a referral if we can’t
- The first fix we would make, which you can use either way
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