Production AI systems for growing businesses.

We design for failure first. Every production system gets an evaluation harness with regression tests, a confidence threshold below which the model defers to a human, guardrails on output structure, and source-grounded retrieval where applicable. We measure accuracy, drift, and refusal rate continuously, not just at launch. If a model cannot pass eval, it does not ship.

No. We build behind an abstraction layer so you can swap models, including OpenAI, Anthropic, open-source, or fine-tuned models, without rewriting application logic. Deployment is cloud-agnostic: AWS, GCP, Azure, or your own hardware. We are an AWS Partner because most clients prefer it, not because we depend on it.

We treat data residency, redaction, and audit trails as architecture decisions, not afterthoughts. PII gets masked before it reaches third-party models, or we run private and local models where regulation requires it. We have shipped systems against fintech, healthcare, and EU privacy constraints. We implement the controls; your compliance team owns the certifications.

You do. Full IP transfer is the default: code, prompts, fine-tuned weights, datasets, infrastructure-as-code, documentation. We do not keep back-doors, license-locked components, or proprietary frameworks you need us to maintain. Repos are yours from day one.

We slot in. That can look like a parallel pod owning a workstream, embedded engineers in your sprints, or a discovery-and-build team that hands off to your in-house group. We document as we go, run reviews with your leads, and aim for your team to maintain the system long after we are gone.

We default to human-in-the-loop wherever the cost of being wrong is higher than the cost of being slow: clinical, financial, legal, or customer-facing decisions. Confidence scores route uncertain cases to reviewers, and we instrument those reviews so the model learns from them over time. Full automation is earned, not assumed.

RAG when the answer lives in documents or databases and freshness matters. Fine-tuning when behaviour or format needs to be consistent and prompt engineering hits its ceiling. Plain prompting plus structured outputs when a frontier model already does the job well. Most production systems we ship are a hybrid, and we measure to decide, not guess.

We agree on the business metric before we write code: hours saved, cycle time reduced, conversion lifted, error rate dropped. Every prototype goes through a go/no-go review against that metric before it earns a production budget. We have told clients not to ship features that did not move the number, then rescoped from there.

Launch is a milestone, not the finish line. Every system ships with monitoring for latency, accuracy, drift, and cost-per-task, plus alerting, rollback paths, and a retraining cadence. You can hand it to your team, because we document for that, or keep us on a retainer for monitoring, eval refresh, and incremental improvements.

Global team operating from India with active engagements across the US, UK, Europe, the Middle East, and Asia: 11 countries to date. We sign mutual NDAs and DPAs before discovery, support customer-jurisdiction contracts, and align working hours to your team core overlap. Cross-border IP transfer and data-handling clauses are routine for us.

Often SaaS. We start every conversation by mapping your use case against existing vendor AI. If a SaaS tool solves 80% of your problem, we'll tell you to buy that.