Hire AI developers in 48 hours.
Our AI engineers ship LLM features that survive contact with production: RAG pipelines with retrieval quality measurement, copilots with guardrails, and document intelligence with human-in-the-loop review. We run AI workflows in production inside Garuda, our own clinic management platform — so we build for reliability and cost, not just the demo.
- 48h
- 5–10y
- Full-time
- Replace
What our AI engineers deliver.
RAG & knowledge assistants
Retrieval-augmented assistants over your documents and data — chunking strategy, vector search, citation-grounded answers.
AI copilots
In-product copilots that draft, summarise and act — with permission boundaries, audit trails and fallbacks.
Document intelligence
Extraction and classification over invoices, contracts and clinical records, with confidence scoring and review queues.
AI workflow automation
LLM steps embedded in business workflows — the pattern we run in production in Garuda's AI Workflows module.
Work our AI team has shipped.
AI Workflows module inside Garuda, our in-house clinic management platform — automating intake, documentation and follow-up tasks
Running in production clinics as one of Garuda's 8 live modules
AI-integrated iOS proof-of-concept for a property business (Blue Lotus Properties)
Validated the AI feature direction before committing to a full build
Contract review assistant extracting clauses and flagging deviations against playbooks
First-pass review time cut from hours to minutes with lawyer sign-off retained
Engineers you'll work with.
Representative profiles from our AI pool. Every engineer is CTO-reviewed before being presented to a client.
- Built a citation-grounded RAG platform over 200k internal documents with measured retrieval quality (evals in CI)
- Shipped an LLM document-extraction pipeline with confidence-based routing — 80% straight-through, the rest to human review
- Led a team of 6 to migrate a legacy monolith to microservices — zero-downtime over 4 months
- Cut TTFB by 60% on a AI platform serving 500k MAU via caching and code-splitting
* Representative composites. We send you 3 real profiles with CVs and GitHub within 24h of your request.
20+ years shipping production software. Built Kuyil AI (our AI assistant platform) and Garuda (clinic management SaaS) — along with 500+ client projects across SaaS, HealthTech, FinTech, Logistics and eCommerce.
Personally reviews every engagement’s first sprint: architecture, code quality, delivery discipline. Not a sales handoff. The CTO stays in the room.
Demonstrating Kuyil AI · Dubai Tech Summit
Tell us what you need. We send you 3 hand-picked AI profiles within 24 hours.
No commitment until you choose to onboard. Interview and test before you decide.
- 1Tell us your requirementsStack specifics, seniority, timezone needs, and what you're building with AI.
- 23 profiles within 24hHand-picked engineers matched to your requirements — with CVs, GitHub profiles and sample work.
- 3Interview & testTechnical interview and optional paid test sprint before any commitment.
- 4Onboarded in 7 daysInto your standups, repos and sprint structure.
- ✓ No sales pressure✓ Reply in 24h✓ NDA available
The three pillars our pods plug into.
Hire engineers in adjacent stacks.
Frequently asked questions
Can I interview developers before hiring?
Absolutely. You interview, optionally run a paid test sprint, and only commit when you're satisfied. No pressure.
What pricing models do you offer for AI engineers?
Monthly retainer for ongoing work, starting at $10k/month for a single senior engineer. Hourly for short engagements. Fixed-price for project-scoped work.
What tools do your AI developers use?
Jira, GitHub, GitLab, Linear, Slack, Notion — we adapt to your stack, not the other way around.
Are developers full-time on our project?
Yes — dedicated full-time, not shared across clients.
Can we scale up or down?
Scale up with new engineers onboarded in 48h. Scale down with two weeks notice. No contractual lock-in.
Which models and providers do you work with?
OpenAI, Anthropic, and the Azure OpenAI / AWS Bedrock managed offerings, plus open-weight models where data residency requires it. We pick per use case — and we'll tell you when a cheaper, smaller model is the right answer.
How do you handle hallucinations and quality?
Evaluation sets before launch, citation-grounded retrieval for factual answers, guardrails on output, and human-in-the-loop review queues for high-stakes actions. Quality is measured, not assumed.
Can you deploy in our cloud for data privacy?
Yes. We deploy RAG and LLM pipelines inside your AWS or Azure account, with your data never leaving your tenancy. PII redaction and access controls are part of the design, not an afterthought.