(AI & data)

AI and data work that survives contact with your real numbers

AI and data services for organisations whose figures live in six places and agree in none of them. Pipelines, models, reporting and the database underneath — built so the answer is the same whoever asks.

Talk to an engineer What we deliver
Data mesh abstract
AI & data

(Where this usually starts)

Most AI projects stall on data rather than on models. The training set has gaps nobody documented, the definition of an active customer differs by department, and no one can say where yesterday’s figure came from. Fix that and a modest model earns its keep. Skip it and a sophisticated one produces confident nonsense at speed.

So the first phase is usually the unglamorous half — sources, definitions, ownership — with something working in front of people early enough that they can argue with it.

(Services)

Everything in AI and data

/

(What we deliver)

What our AI and data services cover

Six strands of work. Most clients need two of them at once, and the order matters more than the shopping list.

  1. Deciding which problems are worth a model at all, then building the ones that are: classification, extraction, forecasting, assistants grounded in your own documents. Each comes with an evaluation set and a written account of what it gets wrong.

  2. Framing the question, building features that hold up outside the training window, and proving the model beats the rule of thumb it replaces. Sometimes it does not, and you are told early rather than at the demo.

  3. Ingestion, ELT and a warehouse worth querying — Snowflake, BigQuery or Databricks, modelled in dbt and orchestrated in Airflow. Quality tests sit between the pipeline and the dashboard so bad rows stop before anyone reads them.

  4. Spark, Kafka, partitioning and file layout for the volumes where a single database stops coping. The review that comes first often finds you are below that line, which saves you a cluster.

  5. Power BI, Tableau and Looker on top of a semantic model where each metric has one agreed definition. Dashboards are built around a decision someone makes weekly, then checked against usage a month later.

  6. Performance tuning on PostgreSQL, SQL Server and MongoDB, backups proven by a timed restore, failover you have rehearsed, and version upgrades done without a weekend outage. Retention and access rules are written down so an audit question has an answer.

(How we work)

How an AI or data engagement runs

Data work fails when the first visible output arrives six months in. These phases are ordered so something is checkable within weeks.

  1. 01

    Data audit

    A fortnight with your sources: what exists, who owns it, how complete it is, and which definitions disagree. You get a written map and an honest view of what can be answered today.

  2. 02

    Framing

    The decision the work has to improve, the measure of success, and the evaluation set held back from the start. Anything that cannot be measured this way gets dropped here rather than later.

  3. 03

    Baseline

    The simplest thing that could work — a rule, a query, an average. Every later model is judged against it, and occasionally the baseline wins and the project stops, cheaply.

  4. 04

    Build

    Pipelines, models or reports in two-week increments, against real data in a real environment. Each increment ends with something you can open rather than a status report.

  5. 05

    Evaluation

    Accuracy where it matters, errors grouped by type, and the cases the system should refuse. For anything generative that includes checks against hallucination and a record of what the model was allowed to see.

  6. 06

    Run

    Monitoring for drift, freshness and cost, alerts that name the table or the model, and a retraining plan with a person’s name on it. Documentation lives in your repositories.

(Why Team of Keys)

How we keep AI and data projects honest

The failure modes are well known: a model nobody can evaluate, a warehouse nobody queries, a dashboard with four versions of revenue. These are the habits that avoid them.

  1. 01

    A baseline before a model

    Every project starts with the simplest approach that might work. If a threshold and a rules engine get you most of the value, that is what you are told, and the saving is yours.

  2. 02

    Evaluation agreed up front

    A held-back set and a success measure are fixed before modelling starts, so nobody gets to pick a flattering metric afterwards. Error analysis is part of the deliverable.

  3. 03

    Privacy designed in

    Personal data is minimised, masked in non-production and kept out of third-party models unless you have decided otherwise in writing. Where the data cannot leave your tenancy, the architecture reflects that.

  4. 04

    Definitions, not dashboards

    Metrics are defined once in a semantic model and reused everywhere. It is slower in week one and it is the only thing that stops the numbers drifting apart again.

  5. 05

    Your platform, your accounts

    Warehouses, repositories, pipelines and model registries sit in your own cloud accounts from the first week, under your billing and your access controls. Handover becomes a documentation exercise rather than a negotiation.

(FAQ)

Questions, answered

A data audit is a fixed, small price and takes a fortnight. After that each phase is quoted against an agreed scope: a first warehouse with a handful of sources is a different order of cost from a streaming platform or a production model with monitoring. You see the phase plan and the price before anything is built.

Not always, though you usually need the data to be findable and consistent. A single well-understood source can support a first model. A recommendation or forecasting system spanning several systems will need a warehouse, because otherwise you spend the project reconciling inputs instead of improving the model.

Yes, and that is the common case. Existing pipelines, an SSIS estate, a Redshift cluster or a Power BI tenancy all get reviewed first. Parts worth keeping are kept, and anything we propose replacing comes with the reasoning and the cost of each option.

By grounding answers in your own content, citing the source for each claim, and testing against a fixed evaluation set that includes questions the system should refuse. Confidence thresholds route uncertain cases to a person. For anything with legal or financial consequence, a human approves before the action is taken.

The audit produces a usable map of your data in about two weeks. A first pipeline or a baseline model that people can judge normally lands within six to eight weeks. Production monitoring, retraining and the reporting layer follow in later phases rather than being promised at the start.

(Global presence)

Nine countries, one studio behind them.

Every project is designed, built and shipped from one studio.
Turn the globe, or pick a country to see what we deliver there.

NoidaDrag to turn

Studio · Noida, India · --:--

(Next step)

Tell us which number nobody trusts

Send the systems involved, the question you cannot answer today, and the deadline. You get an honest read on whether the data supports it, and a phase plan, usually inside two working days.

START

Or write to info@teamofkeys.com · Noida, India