Assessment
A fixed-scope, fixed-fee diagnostic against your real systems and your real bill. Ends in a written answer, including the answer that no build is justified. 2–3 weeks.
01 / Foundations
We build the foundations that turn fragmented information into infrastructure a business can actually depend on.
02 / Efficiency
Most AI systems do not fail on quality. They fail on latency nobody measured and a bill nobody modelled.
03 / Ownership
Documentation, tests and monitoring are part of the work, so your team owns the system instead of inheriting a black box.
Shahnawaz Akhtar & Co.
Data engineering and inference engineering for growing companies, engineered to stay reliable under load and to cost what you expected.
What we do
Most engagements start in one of the first two: the data platform underneath, or the inference layer on top. Each service page sets out the problem, the decisions involved, and the methods we actually apply.
Foundation
Design and build data platforms that stay correct under load, under change, and under cost pressure, so the analytics and AI built on top of them can be trusted.
Foundation
Serving-layer engineering for teams whose AI feature works but costs too much, responds too slowly, or falls over under real traffic.
Capability
Retrieval, agents and workflow automation built with the evaluation, guardrails and monitoring that a demo never needed and production cannot go without.
Capability
Feature pipelines, deployment and monitoring for ML systems that must stay accurate as the world they were trained on moves.
Capability
Demand and capacity forecasting, optimisation and scenario models, built around the decision they feed rather than the accuracy score they report.
Capability
Architecture decisions, vendor selection, roadmap sequencing and project recovery, for organisations that need the judgement more often than they need the headcount.
Our point of view
Every engagement should end in a production decision, a working system, or a documented reason not to proceed. Kill criteria are agreed in writing before the work starts, and improvements are measured before and after, not asserted at the end.
How we work
Each is independently useful, and each is scoped so you can stop after it without having wasted the money.
See how we work ↗A fixed-scope, fixed-fee diagnostic against your real systems and your real bill. Ends in a written answer, including the answer that no build is justified. 2–3 weeks.
Implementation inside your stack, reviewed by your team, shipped incrementally, with before and after numbers on every change that claimed an improvement. 4 weeks – 6 months.
Retained review and technical direction as the system evolves, with deliberate handover so the dependency shrinks over time. 1–2 days per week.
The company
Shahnawaz Akhtar & Co. is a data and AI engineering practice for growing organisations. We build the platforms, pipelines and serving systems that everything else depends on, and advise on the decisions that are costly to reverse.
Engagements are led by the people who deliver them. Whoever scopes the work is in the code review, with no hand-off to a delivery team you have not met. Where an engagement needs capability beyond the engagement lead, we bring in engineers we have worked with directly, named to you before they start.
We begin with the decision, workflow, or economic outcome, not the technology.
Evaluation, reliability, security, and maintainability are built in from the start.
What a system costs to run is a design constraint, measured and reported, not a surprise that arrives with the invoice.
We transfer capability, documentation, and confidence rather than creating dependency.
Begin a conversation
Tell us what you are building, what is blocking progress, and what a successful outcome would look like. If we are not the right people for it, we will tell you that too.