Custom AI development
We translate business requirements into production AI systems: fine-tuned models, retrieval pipelines, agentic workflows and complete applications — evaluated against your data, deployed on your infrastructure, and supported after launch.
Request a pilot on your dataThe problem
Pilot purgatory
Proofs of concept impress in a meeting and stall before production.
No evaluation
Without benchmarks on your own data, nobody can say whether a model is good enough.
Vendor lock-in
Closed platforms own your prompts, data and roadmap.
Local context
Azerbaijani language, local regulation and domain vocabulary need deliberate work.
What you get
Domain adaptation of open models on your data, with evaluation sets and benchmarks built before training starts.
Agents that read, decide and act across your systems — with approval gates, audit logs and cost tracking.
Full applications from discovery to support — web, API and integrations — including content tools such as human-approved AI avatar video for training and communications.
How it works
Deployment & trust
You own the result: the code, the evaluation sets and the deployment. Models run on your infrastructure, and every system ships with monitoring and a support plan.
Who it’s for
A product that moves a real metric, with a clear cost to run.
Private deployment, open components and no lock-in.
Evaluation, MLOps and a codebase you can own.
A partner that takes pilots all the way to production.
In oil, gas & energy
Automate review of technical submittals, MoCs and inspection reports.
Combine sensor, maintenance and HSE data into decision support.
Custom models for risk, compliance and customer operations.
FAQ
It depends on scope and data readiness. We agree milestones after discovery; an evaluation-backed prototype usually comes first.
For private deployment, yes — we size and can supply the hardware through our IT practice, or start on a hosted environment you control.
Related
Cited answers from your documents and SOPs, on an Azerbaijani-native language model inside your network.
Pressure-test AI assistants with synthetic users, run autonomous regression QA, and pentest behind the login.
Not sure where to start? A fixed-scope discovery sprint maps your use cases first.
A pilot runs on your documents, calls or records — measured against criteria we agree up front.
