Consulting

Hands-on AI engineering, from design to handover.

I work with engineering and product leaders on the parts of AI that decide whether it works in production: AI-native builds, adoption and governance, and the data foundations underneath GenAI.

What I do

Four engagement types. Each starts with a fixed-scope two-week sprint, so you can judge the fit and see a concrete result before committing to a longer build or a monthly engagement.

AI-native product and technology partnership

I work as your technical partner to design and build an AI-native product or feature, including moving a legacy system to an AI-native architecture. Product thinking, architecture, and hands-on delivery in one engagement.

  • Product and architecture definition
  • Working AI-native build (LLM, GenAI, agents)
  • Legacy-to-AI-native migration plan
  • Handover and team mentorship
Typical length: 2-week design sprint, then a 3–6 month build

AI adoption and advisory

For teams that need to decide where AI fits and how to run it safely. An assessment, a prioritised roadmap, evaluation and governance design, and mentoring so your engineers can keep it running.

  • AI opportunity and readiness assessment
  • Evaluation, monitoring and governance design
  • Agent security review
  • Senior engineering and team mentorship
Typical length: 2-week assessment sprint, then optional monthly advisory

Forward-deployed engineering

I embed with your team, on-site or remote, work inside your systems and data, ship a production AI capability, and document the repeatable pattern before I leave.

  • Hands-on build inside your environment
  • Production-ready AI capability (agents, RAG, evaluation)
  • Documented deployment pattern your team can repeat
  • Pairing and mentorship with your engineers
Typical length: Monthly, minimum 3 months, after a 2-week scoping sprint

Data foundations for GenAI

The data layer that GenAI depends on: data engineering, knowledge graphs and ontology design, and the retrieval and LLM layer on top, across Databricks, Snowflake, AWS, Azure and GCP.

  • Data engineering and cloud platform design (multi-cloud)
  • Knowledge graph and ontology modelling
  • RAG and LLM integration
  • Data quality and lineage for AI use
Typical length: 2-week data assessment, then 6–12 week build phases

Who this is for

Teams with an AI system in or near production, or a legacy platform they want to make AI-native, that need it to be reliable, secure and explainable to their own risk and audit teams.

Where the experience comes from

Roles and engagements across the following domains. Client names are not published.

Healthcare & life sciencesPharmaEnergy & utilitiesBanking & fintechTechnology & high-techManagement consulting

Work has included full-stack AI engineering and research, product building, advisory, senior engineering, team mentorship, AI adoption, native AI solutions, and legacy-to-modern data and AI migration.

How an engagement works

  1. A 30-minute call to understand your system, constraints and goals.
  2. A written scope for a fixed two-week sprint, with a defined deliverable.
  3. Work in your repo or environment, with weekly written updates. After the sprint, you choose a longer build, a monthly engagement, or to stop.
  4. A handover: code, documentation and a walkthrough, so you can run it without me.

Open-source and products behind the work

I maintain open-source projects (AgentMesh, Bulwark, Keelgate, Tycheon and ARGUS-AI, all Apache-2.0) and build AI products. See the products page. ARGUS-AI is a Beta library of rule-based heuristics; the commercial ARGUS Platform is planned and not available.

Questions

Who do you work with?

Engineering and product leaders at organisations in healthcare and life sciences, energy and utilities, banking and fintech, and technology, where AI mistakes have real consequences.

Do you build or only advise?

Both. The engagements above range from advisory only to forward-deployed builds where I write and review code inside your team.

Do your tools make us compliant?

No. Open-source tools such as Bulwark and AgentMesh are designed to support audit-trail and access-control needs, but they do not provide or certify regulatory compliance. Compliance is established by your own programme and auditors.

Can you work in our cloud?

Yes. I work in Databricks, Snowflake, AWS, Azure and GCP environments, including multi-cloud setups.

How do we start?

Send a short note through the contact page. If there is a fit, we book a 30-minute call and I follow up with a written scope.

Start a conversation

Tell me what you are working on and I will reply with whether and how I can help.

Contact Ambharii Labs →