Accelerators
›Purpose-built LLM systems, multi-agent reasoning workflows, and domain-adapted models.
Universal semantic metric layers, natural language querying, and executive intelligence.
Mathematical constraint solvers (MILP, CP-SAT) for scheduling, allocation, and routing.
Pragmatic ERP modernisation, event-driven API mesh, and custom operational applications.
The Supply Chain AI Prompt Library
55 copy-paste prompts for planners, buyers and OR analysts. Use them with Claude, ChatGPT or Gemini.
Business Intelligence Consulting for Mid-Market Companies: One Set of Numbers, Ready for AI
Our business intelligence consulting services define the numbers your leadership runs on, build the data model and dashboards that deliver them, and govern them so every team, and every AI assistant, gets the same answer. We do this on the BI tool you already own, whether that is Power BI, Tableau or Looker. The result is a board pack built from live data instead of a month-end spreadsheet.
We review your sources, models and reports, score them on seven checks, and hand you a ranked fix list.
What our business intelligence consulting services include
Business intelligence consulting services cover six pieces of work: deciding which metrics matter, getting the data into one model, certifying the KPI definitions, building dashboards tied to decisions, opening the data to self-service and plain-English questions, and governing all of it. Most firms need some of these more than others, and the assessment tells you which.
KPI and decision framework
Data model, warehouse and pipelines
Semantic layer and certified KPIs
Decision dashboards with alerts
Self-service and natural language analytics
Governance
Dashboards from our Power BI portfolio
How an engagement runs
Every engagement moves through three steps: assess, build, then adopt and hand off. The assessment decides what gets built, the build delivers the first decision dashboards on certified data, and the handoff makes sure your team can run it without us. We agree durations with you after the assessment, once we have seen the data.
Assess
Build
Adopt and hand off
Power BI and Tableau consulting on the tool you already own
You do not need a new BI tool to get trustworthy numbers. We work in Power BI, Tableau and Looker, on data in Snowflake or in the ERP's own database, including SAP HANA. As a Salesforce partner, we also bring CRM pipeline and account data into the same model. We recommend a migration only when the current tool blocks the work.
Results from our BI work
Our BI consulting is judged by whether leadership stops arguing about the numbers and starts using them. Across our BI work, that has meant retiring most slide decks for live executive dashboards, raising sales hit-rates with daily recommendations, and cutting empty miles with a shared map. Three examples, by company type:
Executive dashboards, multi-sector
FMCG distribution
Logistics
At a renewable energy operator, a client-facing Tableau portal replaced daily CSV files, and data latency now averages 10 minutes. More in our case studies.
Is your data ready for AI-generated SQL?
AI can now write long SQL in minutes, but speed without control is a liability. Before you put an AI assistant on your warehouse, check that its queries bind to certified definitions, respect access rules, and leave an audit trail a human can review. A wrong number that arrives fast still gets used.
Why this is coming up now
Assess first or pilot first?
The one-page AI data-readiness scorecard
The AI data-readiness scorecard checks seven controls an AI assistant needs before it queries your warehouse: certified definitions, owned metadata, access rules, SQL validation, human review, logging and data quality. Score each row 1 if the control is absent, 3 if it is partial, and 5 if it is enforced by default.
- Certified KPI definitions exist and the AI is bound to them through the semantic layer.
- Every table and column the AI uses maps to catalog metadata and a named owner.
- Sensitive fields are classified, and access rules apply before a query runs.
- Generated SQL is validated, by parsing and a sample or sandbox run, before results reach a user.
- High-impact or unusual queries route to a human reviewer.
- Every prompt, query, answer and approval is logged, giving an audit trail and lineage.
- Data freshness and quality checks run on every source the AI reads.
As a rule of thumb, treat any row below 3 as a gap to fix before rollout. These seven checks are the core of text-to-SQL governance. For the failure modes behind them, read why text-to-SQL fails in production.
Who this is for
Our BI consulting fits mid-market companies, roughly ₹1,000 to ₹2,000 crore or $120M to $240M in revenue, that already own a BI tool but still argue about the numbers. The typical signs are scattered source systems, an Excel board pack and an AI assistant on the roadmap. Specifically, where:
Most of our BI work is in manufacturing and retail and FMCG, plus logistics and energy. For dashboards that drive action rather than just report, see from BI dashboard to decision engine and why your dashboard is lying to you. Our wider enterprise AI work builds on the same governed data.
FAQ
What do business intelligence consultants do?
How is BI consulting different from buying a BI tool?
Can you work with our existing Power BI or Tableau setup?
What is a semantic layer and why does it matter for AI?
How do we know if our data is ready for AI-generated SQL?
Should we assess our data first or start with an AI pilot?
Do you work with Snowflake and Salesforce data?
Book a BI and data-readiness assessment
We review your sources, models and reports, score them on the seven checks above, and hand you a ranked fix list.
