Zohaib Aamir

Services

Engagements, by the problem they solve.

Four offers. No prices or fixed durations; each starts with a conversation about what you have and what is in the way.

What I build

Four kinds of problem I take on.

Who each one is for, what you have now and what you end up with, and the system on this site that proves it.

01

Data platform and quality foundation

For teams whose reports disagree with each other, whose pipelines fail quietly, or whose data is not ready for anything AI.

What I deliver
  • Ingestion and orchestration (Data Factory, Databricks, Fabric)
  • A validation model: temporal, duplicate, validity and calendar checks
  • Warehouse or lakehouse model with a gold layer and one definition per metric
  • Runbooks and a handover the team can operate without me

Before

  • Deliveries land unchecked and errors surface in a report weeks later
  • One metric, three definitions, depending on who built the dashboard
  • Nobody owns a bad file, so the same error keeps coming back

After

  • One landing zone with named checks at the door
  • A failed check becomes a ticket owned by whoever sent the data
  • One semantic layer that the dashboards and the agents both read

02

Retrieval and knowledge systems

For teams whose answers exist in documents, tickets or internal pages that nobody can query.

What I deliver
  • Extraction from the systems the knowledge actually lives in
  • Embeddings and a vector store (pgvector, Azure AI Search), with scope filtered inside the query
  • A golden set of known answers that retrieval is measured against
  • Sync, tracing and alerting so the index stays current

Before

  • A specialist searches by hand and a first reply takes days or weeks
  • The same question goes to the same three people every time
  • Nobody can say which version of a document is current

After

  • A question in plain language, an answer with its source attached
  • A match score that says when the top result is not good enough to use
  • An index that syncs daily and raises an alert when the sync fails

03

AI agents for operational workflows

For workflows where the work is reading, routing, looking things up across systems and deciding, at a volume a team cannot keep up with.

What I deliver
  • Supervisor and worker agents (LangGraph, LangChain) with one job each
  • Text to SQL over the warehouse gold layer, with scope in the WHERE clause
  • Tool and MCP integrations that pass the user's own authorisation through
  • Tracing per run (LangSmith), an audit log per answer, human review where it matters

Before

  • Requests queue at head office and wait for whoever is free
  • An engineer reads hundreds of clauses by hand before anything is built
  • The answer depends on who is on shift and how long they have

After

  • A supervisor routes each request to retrieval, to SQL, to an action or to a person
  • Every output carries the evidence behind it
  • A qualified person signs anything that reaches a customer

04

Pilot to production

For teams with a prototype that works in a demo and has no path to running unattended.

What I deliver
  • Evaluation design: golden sets, labelled corrections from reviewers, acceptance rate
  • Scope, guardrails and the paths where the system declines to answer
  • Observability, cost and latency review, connection pooling and rate-limit handling
  • Documentation, runbooks and a handover to the team that will run it

Before

  • The output looks right and nobody can say how often
  • Any user can reach any record, because scope was never designed
  • A prompt change ships and nobody knows whether it made things worse

After

  • An evaluation set that can fail a release
  • Permissions resolved once, tracing on every run, alerts on failure
  • A release gate: previously signed work re-run after every change

How the two halves connect

One architecture, from source system to signed answer.

Select a stage to see what it does and which of the systems on this site it comes from. A composite: no single project has every box, and every box is in at least one.

Bands scroll sideways.

Datasource to one definition
AIrequest to signed answer
Evaluationruns under both

Quality gate

Temporal, duplicate, validity and calendar checks run where the data lands. A failed check raises an alert with a severity and becomes a ticket owned by the source. Nothing downstream refreshes on data that did not pass.

The data band is read by both the dashboards and the agents. The evaluation band is what separates a demo from a system someone will sign off: every model output has a check that can fail.

Contact

Discuss a project.

Tell me what you are building and what is in the way.

Elsewhere

Opens in your mail client, addressed to zohaib@zohaibaamir.com.