Demand forecasting
Forecasts by product, location and channel that feed straight into planning and replenishment.
Analytics & Decision Intelligence
Analytics and models that change real decisions.
We build forecasting, pricing, churn and risk models, and put the answers where decisions get made: in the tools your teams already use, not in a report nobody opens.
The problem
Why this work usually stalls.
01
Forecasts built on last year
Planning still runs on spreadsheets that copy last year's numbers, so stock, staff and spend end up in the wrong place.
02
Dashboards nobody opens
Teams have dozens of dashboards but still ask an analyst for the number before a decision.
03
Models stuck in notebooks
Data scientists build good models that never reach the system where the decision happens.
04
No way to test changes
Price, offer and process changes go live without a proper test, so nobody knows what worked.
What we deliver
Analytics & Decision Intelligence, built for production.
Pick one piece or the whole programme. Each is scoped to a business metric and shipped, not left as a proof of concept.
Pricing and promotions
Models that estimate how price and offers affect demand and margin, with guardrails your commercial team sets.
Churn and lifetime value
Early warning on customers likely to leave, and the actions most likely to keep them.
Risk and scoring models
Credit, fraud and propensity models with explanations that business and compliance teams can follow.
Decision dashboards
A small set of dashboards built around decisions, with definitions everyone agrees on.
Experimentation
A/B tests and holdouts designed properly, so you know which change caused which result.
How we deliver
From first call to production.
Every phase ends with something you can use, not just something you can read.
Always included
- A senior squad of two to four people, led by an engineer from companies like Amazon, Google or Microsoft
- Weekly demos and one business metric agreed upfront
- Founder review every week and sign-off before production
- All code, models and documentation owned by you
Typical tools
01
Weeks 1 to 2
Decision mapping
We start with the decision, not the model.
- Who decides what, how often, with which data
- Baseline of current accuracy and outcomes
- Agree the metric the model must move
02
Weeks 2 to 3
Model and data design
Features, methods and how results reach users.
- Data checks and feature design
- Method chosen for accuracy and explainability
- Plan for testing against the current approach
03
Weeks 3 to 8
Build and prove
Models run side by side with today's process before anyone relies on them.
- Model in production, in shadow mode first
- Results surfaced in existing tools
- Measured against the baseline
04
Ongoing
Embed and improve
Retraining, monitoring and new decisions over time.
- Accuracy and drift monitoring
- Scheduled retraining
- Next decisions added to the roadmap
Where we apply it
Industries we know well.
Further reading
From the blog.
AI Implementation Cost in India: Drivers, Ranges and Budgeting
A practical guide to AI implementation cost: the real cost drivers, indicative ranges by project type, the running costs people forget and how to budget sensibly.
AI Readiness Assessment: A Practical Checklist for Leaders
A scored AI readiness assessment checklist covering strategy, data, technology, people and governance, with a simple rubric and guidance on what to fix first.
FAQ
Questions we hear a lot.
How accurate will the forecasts be?
It depends on your data history, how stable demand is and how far ahead you forecast. We measure accuracy against your current method on your own data before you rely on anything, and we report it honestly.
Can models run inside our existing tools?
Yes. We prefer putting results where people already work, such as your ERP, CRM or BI tool, rather than adding another app.
Do we need a data science team to maintain this?
No. We set up monitoring and retraining, document everything and train your team. We can also run it for you.
How do you make models explainable?
We choose methods that can be explained where it matters, show the main drivers behind each prediction and document how the model was built and tested.
Other services
Everything between ambition and production.
GenAI & AI Agents
Copilots, retrieval systems and agents wired into your real tools and data.
AI Strategy & Roadmaps
Find where AI moves your numbers and sequence a roadmap people believe in.
Data Platforms & Engineering
Lakehouses, clean pipelines and governed metrics that every model depends on.
MLOps & AI Engineering
CI/CD for models, observability and cost controls that keep AI reliable.
Digital Transformation & AI Governance
Process redesign, change and responsible AI aligned with the DPDP Act and EU AI Act.
Managed Services
Cloud, DevOps, QA and release management, run 24x7 against clear SLAs.
Talk to a founder about Analytics.
Tell us where you are. We will come back within one business day with a point of view, not a sales deck.