Turn the data you already have into forecasts, insights and smarter decisions, with models that run reliably in your day-to-day business rather than in a slide deck.
Live dashboards and alerts built on one source of truth.
Personalise at scale
Recommendations and pricing that adapt to each customer.
Ideal for
Businesses with growing data and decisions still made on gut feel.
For your technical team
Speech, language and decision models running against real workloads — including Telugu-first speech recognition inside a live legislative chamber, which is about as unforgiving as deployment conditions get.
The problem
The benchmark is not the job
A model that scores well on a public benchmark can fail completely on your data, because your data has properties the benchmark does not: your vocabulary, your accents, your document layouts, your edge cases.
Indian-language speech is low-resource relative to English, and off-the-shelf accuracy drops sharply.
Real users code-switch mid-sentence in a way clean training data rarely does.
Domain vocabulary — procedural terms, place names, member names — is exactly what a general model gets wrong.
Accuracy measured once at launch tells you nothing about accuracy six months later.
The pipeline we build
The quality gate sits before serving, not after. A model that has not been scored against a held-out set representative of your real conditions does not reach production.
What you get
Deliverables, not activities.
Each of these is something that exists at the end and that you own.
01
Domain data collection and preparation
Gathering real samples under real conditions, because the gap between benchmark and production is almost always a data gap.
02
Model adaptation
Domain lexicons, targeted fine-tuning and post-processing — applied where they measurably pay, not by default.
03
Evaluation you can inspect
A held-out set, a stated metric, and a scoring method written down so the number means something to your technical reviewers.
04
Inference under constraint
Serving that meets your actual latency and concurrency requirements, including live streaming where the output is needed during the event.
05
Monitoring and drift detection
Continuous measurement in production, with the failures categorised so improvement has somewhere to start.
06
Retraining path
A defined route from "we found a weakness" to "the fix is deployed", rather than a rebuild each time.
Technology
What this runs on.
Grouped the way a technical evaluator reads it. Everything listed is in production in work on this site.
The three things that decide whether this goes well.
Real samples of the data the model will meet — not a curated set.
A clear statement of what "good enough" means for your use, in a number.
Whoever currently does the task by hand, for an hour, to define the edge cases.
Questions we get asked
Do you train models from scratch?
Rarely, and only when adaptation demonstrably will not get there. Most of the value in Indian-language deployment comes from domain adaptation, lexicons and post-processing rather than from a new base model.
What accuracy can we expect?
We will not quote a number before seeing your data, because a number quoted that way is meaningless. We will agree a metric and a held-out set up front, and report against it honestly.
Can this run on our own infrastructure?
Yes. Where residency or procurement requires it, we deploy into your tenancy and hand over the operational runbook.
Start here
Let’s build what’s next.
Tell us where your business wants to grow. In one conversation we will show you where AI and software will pay back first, and whether we are the right team to build it.
We look at your goals, your current systems and your biggest time and cost drains, then recommend the first AI or software move with the fastest payback.
