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
Selected clients we have delivered for
- Projects delivered
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30+
- Professionals trained in AI and data science
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300+
- Industries served
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8
- Years of combined team experience
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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.
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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.
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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.
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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.
Agentic AI
Planner and worker agents with typed tools, bounded retries, cost ceilings and full tracing.
Learn moreRAG and LLM systems
Retrieval over private data, natural language to query and structured extraction, with citations and evaluation.
Learn moreData engineering
Pipelines, lakehouses, vector and graph stores with contracts, quality gates, lineage and access control.
Learn moreMachine learning and forecasting
Forecasting, risk, churn, anomaly detection and recommendation models, shipped with monitoring.
Learn moreAnalytics and BI
Dashboards and semantic layers in Power BI, Qlik, Tableau and Looker, with row-level security.
Learn moreAI readiness assessment
Two to three weeks, fixed scope and price, a written architecture and costed roadmap you keep.
Learn moreAll services, including DevOps, MLOps and custom software development
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.
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
Question: Which regions logged the most site visits last quarter?
- classifyData question, sent to the query path
- databasedemo_field_ops, named in the request
- schemaCached: visits, sites, regions
- policyRead only, passed
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; | Region | Visits |
|---|---|
| North East | 412 |
| Midwest | 356 |
| South | 298 |
| West | 241 |
Answer: The North East logged the most visits last quarter, 412, followed by the Midwest with 356.
Question: And only for oncology sites?
- classifyFollow-up, resolved against the last question
- resolvedSite visits last quarter by region, oncology sites only
- schemaReused from the last question
- policyRead only, passed
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'
AND s.therapy_area = 'oncology'
GROUP BY r.name
ORDER BY visits DESC; | Region | Visits |
|---|---|
| North East | 138 |
| South | 121 |
| Midwest | 97 |
| West | 64 |
Answer: For oncology sites the order changes: the North East still leads with 138, and the South moves to second with 121.
Question: Delete last month's visit records.
- classifyData question, sent to the query path
- generatedThe model produced a DELETE statement
- policyBlocked in the query layer: writes are not allowed
DELETE FROM visits
WHERE visited_at >= DATE '2026-09-01'; Answer: This assistant can only read data. The statement never reached the database, and nothing was changed.
Question: What can you do?
- classifyConversation, not a data question
- databaseNot touched
Answer: I answer questions about field operations in demo_field_ops: visits, sites and regions. I can only read, and I show the query behind every answer.
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.
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 studyAn 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 studyClinical 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 studyConversational 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 studyWhere 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.
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Phase 01
Assess
- Two to three weeks, fixed price
- Architecture, risk register, costed roadmap
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Phase 02
Foundation
- Pipelines, contracts, access model
- Quality gates before any model
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Phase 03
Build
- Reviewable increments
- Evaluation set agreed up front
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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.
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.
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.
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.
- 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
Insights
Notes from production
Saudi PDPL for AI systems: a checklist for banks
What the Saudi Personal Data Protection Law means for AI in banking: impact assessments, transfers outside the Kingdom, breach notice, credit data, logs.
Read-only by policy: making text-to-query safe in a regulated data estate
Why a prompt that says 'only read' is not a control, and the five design rules we used to ship natural language analytics over life sciences data.
What production-ready means for an agentic AI system: a checklist
The checks we run before an agentic AI system goes live: tool contracts, budgets, evaluation, tracing, human approval and a clean way to swap models.
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.




