Nazmi

Services

Three fields, shipped to production.

Each pillar below is real, shipped work — with the engineering discipline to get it into production and handed over to your team. Here is what an engagement looks like.

AI agents & automation

Take the repetitive work your team does by hand — reading documents, answering questions, updating records — and hand it to agents you can trust, tested before they reach anyone.

  • Multi-agent systems and agentic orchestration — agents that coordinate across tools and steps
  • Retrieval (RAG) over your own documents, with answers that cite their sources
  • Agents that act in your systems — update records, file tickets, not just answer
  • Persistent agent memory — recalls prior facts and decisions across sessions, instead of starting cold each time
  • Knowledge graphs that answer questions spanning many records at once — the ones plain search can't
  • Evaluation that scores answers before they ship, and guardrails that stop the unsafe ones

Shipped: AI agents for a retail-technology product, and a RAG support assistant taken to production.

multi-agent systems · knowledge graphs · agent memory · agent evaluation

Production ML & MLOps

Your model works in a notebook but not in production. Get it deployed, monitored, and handed to your team to run — and keep it working as the data changes.

  • A handover your team can run and extend — not a black box you depend on us for
  • Monitoring that catches a model going stale before it shows up in your numbers
  • Automated retrain-and-deploy when drift or new data calls for it, with versioned models you can roll back in minutes
  • Recommender, ranking, and anomaly-detection systems tuned to a business metric, not just an offline score

Shipped: standardized enterprise-telecom ML workflows on Azure ML and Databricks.

Azure ML · AWS · GCP · Databricks · MLOps · recommenders · ranking · anomaly detection

Private & on-prem AI

On-prem / private

When data cannot leave your infrastructure or API costs will not scale: distill and post-train small task-specific models, accelerate inference, and deploy on-prem or at the edge.

  • Nothing leaves your network — no outside API in the path, so residency and compliance rules are met
  • A fixed cost you control, instead of per-call bills that climb with usage
  • A small model trained for your one task that matches a big model's quality on it — at a fraction of the size and cost
  • Reinforcement learning and task-specific fine-tuning to optimize a model for your exact workflow
  • Runs on your own servers, with inference tuned to the hardware you already have

Shipped: a 0.8B on-prem SQL agent post-trained to rival far larger models on text-to-SQL.

private LLMs · on-prem deployment · fine-tuning · RL optimization · efficient inference

Typical first engagements

Concrete ways to start when the project or partner route is real.

  • 1–2 weeks

    AI workflow audit

    Clarify the workflow, data, owner, risks, architecture, and delivery plan.

  • 1–2 sessions

    Prototype review or rescue

    Review an existing AI prototype and decide what should be fixed, rebuilt, deployed, or stopped.

  • 4–6 weeks

    Implementation pilot

    Build one useful workflow with testing, deployment, and clear handover.

See the AI Opportunity Audit →

How we can engage

We shape the work around the problem. These are the most common ways to start when the project, partner route, or client demand is real.

  • Paid discovery and roadmap

    A focused scoping engagement that turns a vague AI idea into a buildable plan.

  • Fixed-fee implementation

    A scoped build with clear deliverables, from design to a working system.

  • Monthly delivery capacity

    Ongoing AI implementation support for teams with active build needs.

  • Support & improvement

    Keep a running AI system healthy, measured, and getting better.

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Partner delivery

Nazmi can support technology partners, consulting firms, recruiters, and project platforms when a client needs deeper AI architecture and implementation capacity.

  • Client project support

    Implementation capacity for scoped client work — agents, retrieval, on-prem models, deployment, and handover.

  • Architecture and delivery ownership

    Help turning a client need into a realistic plan, technical architecture, implementation path, and delivery rhythm.

  • Subcontracted or fractional capacity

    Flexible support for serious implementation work without asking the partner or client to hire a full AI team first.

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Move from prototype to deployment.

Tell Nazmi what exists today, what is not working, and what needs to happen next.

Start a conversation or book a 20-minute call →