AI that answers with your numbers, and shows where they came from
GenAI is most useful when it sits on top of the models and data you already trust. We build AI assistants and agents that call your forecasting, attribution and CLV models as tools, search your documents, and give a recommendation a manager can check.
What we deliver
AI agents on your models
An LLM orchestrator that calls your existing models as tools, runs what-if scenarios and explains the answer.
- Tool design over governed tables
- What-if and scenario tools
- Multi-step reasoning with audit trail
Knowledge assistants (RAG)
Ask questions of briefs, contracts, policies and past campaign results and get answers with citations.
- Document ingestion and chunking
- Vector search and re-ranking
- Answers with source links
Machine learning in production
Propensity, CLV, forecasting and MMM models that run on a schedule and feed real decisions.
- Feature pipelines
- Model serving and batch scoring
- Drift monitoring and retraining
LLMOps, evaluation and guardrails
Make GenAI safe to put in front of employees and customers.
- Evaluation sets and quality gates
- Guardrails for PII and tone
- Cost and latency monitoring
Copilots for business teams
Assistants inside the tools your teams already use, from CRM to dashboards.
- Sales and marketing copilots
- Insight summaries on dashboards
- Drafting with human approval
AI strategy and use-case roadmap
Find the use cases worth funding and the order to build them in.
- Use-case scoring by value and feasibility
- Data readiness review
- Pilot-to-production plan
See it working
Three demos from our AI Lab. Each one is an LLM agent that calls real analytical models as tools.
Marketing Intelligence Agent
Which accounts should we prioritise next quarter, and what is the upside?
See how it works →B2C / D2C demoMedia Mix Agent
What happens to revenue if we move 15% of search budget into video?
See how it works →B2B demoPipeline Attribution Agent
Which touchpoints actually create pipeline, and what if we cut events?
See how it works →Our design rule: every number in an answer comes from a tool or a governed table. The language model plans and explains; it never invents figures.
Related case studies
Sorting actionable complaints from noise for a US wireless carrier
Sentiment classification across social platforms so the care team could reach unhappy customers faster.
Read the case study →Finding the drivers of churn for a US telecom provider
Churn segments and models that showed a telecom client why customers were leaving, and where to act first.
Read the case study →Fixing leaky website journeys for a telecom eCommerce team
Clickstream path analysis that found where visitors dropped off, raising conversion and satisfaction.
Read the case study →Have a decision you want to get right?
Tell us the decision, the data you have and the timeline. We will come back with how we would approach it.