ServicesData, AI & Emerging Technology
Data, AI & Emerging Technology
Make decisions from evidence, and automate the work where automation actually pays.
Talk to an engineerWhen companies call us about this
You will probably recognise one of these.
Two departments report different numbers for the same thing.
Sales and finance each have a revenue figure and both are defensible. Board meetings start with an argument about which one is right.
The monthly report takes a week to produce.
Someone exports from four systems, joins them in a spreadsheet and fixes the errors by hand. By the time it is read, it is out of date.
Staff spend their days reading documents a machine could triage.
Invoices, claims, tickets or applications arrive as unstructured text and are sorted manually. The backlog grows every quarter.
You have been asked what your AI strategy is and do not have one.
Vendors are pitching, the board is asking, and nobody can say which of your processes would actually benefit or what it would cost.
What we do
Five things, each with something you keep.
Data platforms
A single, governed store that every report and model draws from, fed reliably from your operational systems. Definitions are written down once, so the numbers agree.
Deliverable: A data warehouse or lakehouse in your cloud account, with pipelines and a data dictionary
Analytics and reporting
Dashboards built around the decisions people actually make, not around what is easy to chart. Each one names its owner and its refresh time.
Deliverable: Production dashboards, a metrics catalogue and the queries behind every figure
Applied machine learning
Forecasting, classification and anomaly detection where there is enough clean data to make them worthwhile. We say so plainly when there is not.
Deliverable: A deployed model with monitoring, plus the evaluation report that justified it
LLM integration
Language models wired into your own documents and systems, with retrieval, guardrails and a human in the loop where the cost of an error is high. Every answer is traceable to its source.
Deliverable: An integrated assistant or pipeline, an evaluation set and a cost-per-request model
Blockchain where it genuinely fits
Shared ledgers for the narrow set of problems where several parties need one record and none of them can be trusted to hold it. For everything else we recommend a database and say why.
Deliverable: A fit assessment, and where it passes, a working ledger integration
What we work with
Chosen for fit, not for fashion.
We choose based on what fits the problem and on what your team can maintain after we leave. A stack nobody in your organisation can operate is a liability, however good it is.
- Cloud platforms
- Languages and runtimes
- Application frameworks
Questions we are usually asked
The ones competitors avoid.
What does an engagement like this cost?
Most engagements land between $30,000 and $140,000. The range is driven by how many source systems feed the platform, the state of the data in them, and whether a model has to be trained or only integrated. A four-week feasibility study is fixed-price and tells you which end of the range you are at.
How long before we see anything working?
A first dashboard on real, reconciled data within four weeks. A working model or assistant, evaluated against your own examples, typically within eight to ten weeks.
Who will actually be on the team?
A data lead with eight or more years across warehousing and machine learning, one or two data engineers, and an ML engineer where a model is in scope. Kunal Khurana, our CTO, is the accountable technical lead.
Who owns the IP?
You do. Pipelines, models, prompts, evaluation sets and every line of code are your property from the moment they are produced. Your data never trains anything that is not yours, and the contract says so.
Tell us what you are building.
A 30-minute call with an engineer who will work on it.A 30-minute call with an engineer who will work on it. No sales sequence, no obligation.
Book a consultationor email us directly