AI and data engineering

From AI-ready to AI‑operational

We build the data foundations, agentic AI and retrieval systems that let healthcare, pharma and financial services teams run AI in production, with evaluation built in.

Built for regulated industries

  • Healthcare
  • Life sciences
  • Financial services
How an Involabs system fits together: your source systems, such as EHR and claims, core banking and documents, feed a data foundation of pipelines, lakehouse, vector stores and access control. That feeds applied intelligence: agents, retrieval, forecasts and knowledge graphs, which produce decisions, answers and reports. Operations and trust, covering evaluation, tracing, cost limits and audit trails, spans every stage.

Selected clients we have delivered for

  • ARASCO
  • Shaker Group
  • Aurelius
  • Metacare
  • Across Borders Alliance of Lawyers (ABAL)
  • Qasatli Real Estate
  • L'ETO
  • Universal Students' Consultancy
Projects delivered

30+

Professionals trained in AI and data science

300+

Industries served

8

Years of combined team experience

10+

The gap we close

Most enterprise AI still stops at the demo

The blocker is rarely the model. It is everything around it. Involabs is built for the part that comes after the prototype.

  1. Data that was never built to be queried

    Fragmented sources, undocumented joins, no lineage and no owner. Every model built on top inherits the ambiguity, and every answer becomes a debate.

  2. AI with no way to prove it works

    No evaluation set, no regression harness, no tracing, no cost ceiling. Quality becomes an opinion, and a silent failure reaches the customer before it reaches the team.

  3. Governance treated as a final step

    In healthcare and financial services, access control, PII handling and auditability are architecture decisions. Retrofitting them costs more than the original build.

What we build

Three layers, delivered as one system

The people who design the pipeline are the people who ship the model and carry the pager. No hand-offs between an AI team, a data team and a platform team.

Need an engineer inside your team next week?

Hire a vetted forward deployed engineer from Involabs. Onboarded within a week, monthly terms from about US$4,000.

Hire an FDE

See it work

Ask a governed data agent

Pick a question. Each answer shows the path it took: how the question was classified, which database it named, the query it ran and the policy that checked it. Try the delete.

  • Writes are blocked in the query layer, not by the prompt
  • Every request names its database
  • The generated query is always visible

The five design rules behind it

Question: Which regions logged the most site visits last quarter?

  1. classifyData question, sent to the query path
  2. databasedemo_field_ops, named in the request
  3. schemaCached: visits, sites, regions
  4. policyRead only, passed
Generated query
SELECT r.name AS region, COUNT(*) AS visits
FROM visits v
JOIN sites s   ON s.id = v.site_id
JOIN regions r ON r.id = s.region_id
WHERE v.visited_at >= DATE '2026-07-01'
  AND v.visited_at <  DATE '2026-10-01'
GROUP BY r.name
ORDER BY visits DESC;
Result
RegionVisits
North East412
Midwest356
South298
West241

Answer: The North East logged the most visits last quarter, 412, followed by the Midwest with 356.

Scripted demo on sample data. No model is called and no client system is involved.

Selected work

Systems in production, not proofs of concept

Life sciences clients are described by sector under confidentiality agreements. Figures come from the systems themselves: test suites, run logs, evaluation sets and cohort sizes.

Pharma and life sciences

Plain-English questions against a governed life sciences database estate

A natural language analytics service for a commercial intelligence platform used by medical and field teams at global pharmaceutical companies.

Result: Read-only by policy: the pipeline cannot produce a write

Read the case study

Financial services

An AI compliance platform for Saudi PDPL, live with banks

A platform that reviews client documents against the Saudi Personal Data Protection Law, scores the gaps and drafts compliant replacements, now in production with banks and finance companies.

Result: 95% content fidelity on AI-drafted documents

Read the case study

Healthcare

Clinical NLP and risk models for a US healthcare analytics provider

Clinical NLP, ICD-10 and HCC coding support, readmission risk and claims cost forecasting over large volumes of unstructured clinical notes.

Result: 500K patient records processed by clinical NLP

Read the case study

Analytics software

Conversational BI for teams with no data team

Connect a spreadsheet or database and come back to a dashboard that already means something, then reshape it by asking in plain language.

Result: 100% of model calls routed through one traced gateway

Read the case study

Where we go deep

Industries where we already know the data and the regulators

We take work outside these sectors. Inside them, we already know the data models, the regulatory expectations and the ways these systems usually fail.

How we work

A short assessment before a long build

Every engagement opens with a fixed-scope assessment. You get a written architecture and a go or no-go recommendation, and you own it whether or not we build the next phase.

How engagements run

  1. Phase 01

    Assess

    • Two to three weeks, fixed price
    • Architecture, risk register, costed roadmap
  2. Phase 02

    Foundation

    • Pipelines, contracts, access model
    • Quality gates before any model
  3. Phase 03

    Build

    • Reviewable increments
    • Evaluation set agreed up front
  4. Phase 04

    Operate

    • Monitoring, tracing, cost ceilings
    • Handover or managed retainer

2 to 3 weeks

For the assessment, before any build commitment.

Fixed

Scope and price on phase one, so the first invoice holds no surprises.

Yours

Code, infrastructure and documentation, in your accounts from day one.

Exit

Every engagement is designed so your team can take it over.

Leadership

Founders who still write the architecture

Delivery teams are small and senior. On most engagements you speak to the people writing the code, and at least one founder stays on the account for its duration.

Meet the team

Usama Waqar

Co-founder and Chief Executive Officer

More than ten years in enterprise business intelligence, data engineering, analytics strategy and data governance. Has built analytics capability for organizations across fintech, retail, healthcare and telecommunications, turning reporting functions into decision systems that executives use.

LinkedIn

Hamza Munir

Co-founder and Chief Technology Officer

More than eight years in data science and AI product development. Has built and scaled machine learning and generative AI systems for companies across retail, healthcare, marketing and finance, and leads the evaluation, observability and delivery standards Involabs engagements run on.

LinkedIn

Training and enablement

We train the teams who will own the system

More than 300 professionals have been through our hands-on programs. Every engagement includes training for the people who will run what we build, and we run standalone programs for enterprise teams.

Advisory and training engagements

  • Data scienceStatistics, machine learning and forecasting on your own data
  • Artificial intelligenceDeep learning, NLP and computer vision foundations
  • Generative AILLMs, retrieval, evaluation and safe deployment
  • Agentic AITool use, guardrails, tracing and multi-agent design
  • Business intelligencePower BI, Qlik and semantic layers for analysts

Start with a two to three week assessment

Send us the problem and the constraints. You get a written architecture, a risk register and a costed roadmap, and you keep them whatever you decide next.