Sales & Distribution
From a depot number to a truck-loading plan.
Which SKUs are declining in Chattogram, by distributor, over the last eight weeks?
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How it works
Natural language in, SQL out.
How did revenue per region trend over the last six quarters?
- 01Our proprietary agentic orchestration interprets the intent and translates it into structured SQL. Nothing else is asked of the model.
- 02Generated SQL is validated against the live schema before it runs, connected read-only to the databases, ERPs, and CRMs already in place.
- 03The database executes the query and returns rows, deterministically, from the company’s own records.
- 04Results render as charts, get pinned into living dashboards, or carry alert conditions that run against fresh data.
- A curated semantic layer maps business vocabulary to real columns
- Asks when a question is ambiguous, instead of guessing
Why every number is traceable
In most AI analytics tools, a language model reads the data and writes the answer in prose, the step where numbers, trends, and entire facts get distorted. Our agentic structure removes that step by design.
The model’s only job is translation: natural language in, SQL out. A query either runs against real tables and columns or fails visibly. Every chart is traceable to the exact query that produced it.
The three engines
Three engines. Independent, composable, built to compound.
Each engine answers the next question on the maturity ladder. A business runs one, some, or all three, configured to what it actually needs, not a fixed bundle.
01
Available today
Answers what happened
Analytics Engine
Turns plain-language questions into live queries, charts, dashboards, and scheduled alerts, grounded in the company’s real schema, with the exact query behind every answer.
You are here
02
In progress
Answers what will happen
Prediction Engine
Builds, trains, and validates real machine-learning models from the user’s own data, guided entirely by prompts, and returns honest held-out metrics with every model.
Explore the Prediction Engine
03
On the roadmap
Answers what should we do
Optimization Engine
Translates business goals and constraints into formal optimization problems, solved by exact methods where optimality is provable and heuristics where scale demands it.
Explore the Optimization EngineAll three share one design principle: the language model never invents an answer. It only translates intent into verifiable computation.
Use cases
The Analytics Engine, applied.
Each case starts with what happened, then carries the same business question into prediction and optimization.
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01 02 Logistics & e-commerceNordkade Fulfilment
The renewal that almost signed itself
The client everyone protected was the only one losing money.
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03 Cold Drink Equipment
Every cooler is a capital bet. Right now it is placed on judgement.
Volume per cooler by outlet class, region, and cooler age.
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04 FinanceSolvenz
Repricing in the dark
A rate cut everyone could see. An outflow only the data could explain.
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05 Pack-Price Architecture
Price, pack, and channel are one decision. They are almost never optimized as one.
Revenue and gross margin per litre by pack, channel, and region, last 12 months.
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06 RetailLarsen & Holt
The deli counter that ran on folklore
Six years of folklore about a deli counter, settled in fifty seconds.
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07 Medical servicesClaraCare
The contract nobody could price
A payer contract priced on a fee schedule that was never actually true.
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08 MicrofinanceUpokul Microfinance
Signal seven
Exposure to the taka, calculated before the storm made landfall.
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Every organization already has the data. What it has instead is a queue.
A manager asks, an analyst translates, a database engineer writes the query, and the answer arrives after the meeting that needed it - or never gets asked at all, because the trip isn’t worth the wait.
The Analytics Engine is a natural-language layer over the databases a company already runs. Ask it directly. Get a chart, an alert, a live dashboard, or a straight answer - on the spot, in plain language, without a ticket.