Build AI that actually ships.
Custom AI software engineered for real business workflows. From use-case validation to production rollout. Owned by you, maintainable by you.
- 2–6w
- ROI-first
- You own
- Free
What we build
Internal copilots for ops, sales, HR, leadership. Permission-aware, role-specific.
Search across docs, SOPs, tickets, wikis. Private, accurate, auditable answers.
Invoices, contracts, KYC, compliance docs. Extract, validate, classify, route.
AI support with human handoff. Integrated with CRMs and ticketing.
Product, content, decision recommendations — at scale, explainable.
Predictive insights for finance, logistics, ops and usage data.
AI products we've shipped
A selection of AI systems built and deployed for clients across legal, SaaS, healthcare and logistics. Details are representative of actual project shapes and outcomes.
Legal document intelligence
UAE law firm
Manual contract review was taking senior associates 3–4 hours per engagement. Clause extraction was inconsistent across teams.
Claude + pgvector + Node.js pipeline that reads contracts, extracts defined clause types, flags anomalies, and produces a structured review summary.
Internal sales copilot
US SaaS company
New sales reps took 6–8 weeks to ramp. Tribal knowledge lived in Salesforce, Notion, and Slack — unsearchable and unstructured.
GPT-4o + RAG pipeline indexed over CRM data, product docs, call transcripts and internal wikis. Role-scoped, permission-aware chat interface embedded in their sales tool.
Healthcare triage bot
Indian hospital group
Appointment scheduling and first-level triage were creating 2–3 hour call queue backlogs. Staff were handling repetitive symptom intake manually.
WhatsApp-integrated NLP bot connected to Garuda EMR. Handles symptom intake, urgency classification, appointment routing and follow-up reminders.
Logistics anomaly detection
Dubai logistics company
Late deliveries were costing ~AED 2M/year in penalties and rebooking. Ops team had no early warning on at-risk shipments.
ML anomaly detection pipeline over GPS telemetry and delivery history. Flags at-risk shipments 4–6 hours before a likely miss, triggering automated re-routing suggestions.
AI tech stack
We pick the right tool for the use case, not the most popular one. Here's the full set of technologies we work with.
- GPT-4o
- Claude 3.5 Sonnet
- Gemini 1.5
- Mistral
- Llama 3
- Pinecone
- pgvector
- Weaviate
- Qdrant
- LangChain
- LlamaIndex
- Haystack
- Custom RAG pipelines
- AWS Bedrock
- Azure OpenAI
- GCP Vertex AI
- Self-hosted on VPC
- LangSmith
- Helicone
- Custom eval harness
- Grafana + Prometheus
- Python
- Pandas
- SQLAlchemy
- Airflow (light pipelines)
Why not just use ChatGPT?
Off-the-shelf AI tools are great for personal productivity. Custom AI is different — it's about owning a business capability that works on your data, in your systems, under your control.
Not sure which path is right for your use case? Book the free AI use-case review — we'll give you a straight answer.
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
From idea to production
- Week 1Discovery & use-case review
Free. Feasibility, value, risks.
- Week 2Architecture & data strategy
Sources, permissions, integrations, security.
- Weeks 3–6MVP development
Rapid build with continuous validation.
- Week 7Hardening
Accuracy testing, monitoring, cost optimization.
- OngoingScale & iterate
Performance, UX, analytics, new capabilities.
Let's validate your AI idea before you build.
Not a sales pitch — a technical and business review. No charge.
- 1Use-case validation & ROI framingIs the use case technically feasible? What's the realistic business impact?
- 2Build vs integrateWhen to use off-the-shelf AI APIs and when to build custom. We'll give you the honest answer.
- 3Data & architectureWhat data sources, integration points, and security requirements shape the build.
- 4MVP scope & estimateRealistic timeline and cost range so you can make a confident build decision.
- ✓ No sales pressure✓ Reply in 24h✓ NDA available
What AI work has looked like in practice.
Scaling delivery velocity for a HealthTech SaaS platform
Clinic & patient records SaaS stuck at 2 engineers. We added a pod, rebuilt CI/CD, and quadrupled shipping speed.
Fleet management ERP — built from scratch in 12 weeks
End-to-end fleet operations platform with route optimisation, driver app and dispatch dashboard for a Middle East logistics group.
Staff augmentation for SaaS accounting platform — Surgical Partners
Ongoing engineering pod expanding development capacity for a SaaS that manages accounting for medical practices. $200k+ engagement, 100% sprint commitment maintained across all delivered milestones.
Want a team, not just an AI build?
Frequently asked questions
Do you build ChatGPT clones?
No. We build custom AI applications designed around your specific business workflows, data, and security requirements. We don't resell wrapper products.
Can you work with our private documents and data?
Yes. We deploy AI in your own cloud infrastructure (AWS, Azure, GCP) with private VPCs, role-based access controls and no data leaving your environment. Your documents never touch a shared training pipeline.
What LLMs and tech stacks do you use?
Cloud-native backends, vector databases (Pinecone, Redis, pgvector, Weaviate), LLM APIs (OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, Mistral) and open-source models for self-hosted deployments. We pick the right model for the use case, not the most popular one.
What's the typical project timeline?
AI use-case review: 60 min, free. AI MVP: 2–6 weeks. Production build with monitoring, evals and guardrails: 8–16 weeks. Ongoing iteration via retainer or dedicated AI pod.
How do you ensure accuracy — what about hallucinations?
Grounding (RAG), confidence thresholds, human-in-the-loop escalation, and output guardrails. We run evals on your actual data before go-live and continuously monitor accuracy in production.
Do you offer ongoing support and monitoring?
Yes. We offer retainers with SLAs, dedicated AI engineering pods, and a managed monitoring service for production AI systems — tracking latency, cost, accuracy, and drift.
What industries have you built AI for?
Healthcare (clinical documentation, diagnosis support), logistics (route optimization, anomaly detection), fintech (document processing, compliance automation), eCommerce (recommendations, support automation) and enterprise SaaS (internal copilots, knowledge search).
Can you integrate AI into our existing product?
Yes. We integrate AI capabilities into existing products via APIs, SDKs, or microservices. We've done this for React, Next.js, Angular, mobile apps, and backend systems in Node, Python, Java and .NET.
What's the free AI use-case review?
A 60-minute call with our CTO. We assess technical feasibility, likely ROI, architecture options, data requirements, and give you a realistic ballpark cost. No obligation and no sales pressure. About 30% of reviews result in an immediate build — the rest become a reference point for future planning.