AI development that ships to production.
Most AI demos never leave the boardroom. We build AI that does: grounded in your data, measured by evals, guarded by guardrails, and running on Soliq, our multi-model orchestration engine.
Global D2C brand · production
Why AI projects fail before they reach users
The model answers from its training data, not yours. It hallucinates with confidence.
Quality was never measured. Nobody knows if it's working, until it visibly isn't.
Edge cases, hostile inputs, sensitive data: handled by hope, not by design.
These are engineering problems, not AI problems. Soliq (our orchestration layer) exists specifically to solve them.
The layer between your product and the foundation models.
Calling one model API gives you one model. Soliq sits above them all (OpenAI, Anthropic, Google, Meta, private models) and adds the layers that let you ship safely: model routing, retrieval, agents, evals, guardrails, governance, audit logs and observability.
- Model selection per task
- Cost vs. quality optimisation
- Fallback chains
- Latency budgets
- RAG on private data
- Input sanitisation
- Output validators
- Sandbox execution
- Eval suites (200–500 cases)
- Nightly production runs
- Drift alerts
- Full audit trail
Model-agnostic by design: swap the underlying model with a config change. Your application never changes.
Six AI capability lines we ship to production.
Retrieval-Augmented Generation (RAG)
Ground AI answers in your private data (documents, knowledge bases, databases) with source attribution and freshness controls.
AI Agents
Agents that take real actions across your tools: search, write, summarise, route, approve, execute, with human-in-the-loop guardrails.
Computer Vision
Quality inspection, object detection, document digitisation, and video analytics at 99%+ accuracy in production environments.
Document AI
Contracts, invoices, KYC documents and medical records, extracted, classified and validated automatically at scale.
Voice AI
Multilingual voice interfaces, call transcription, intent classification and voice-driven workflow automation.
MLOps & Model Governance
Eval pipelines, drift detection, model versioning, deployment automation and audit logs, so you know what shipped and why.
Where we've shipped AI in production.
We work across verticals but go deep in a few. Each industry section below reflects real problems we've solved, not theoretical capabilities.
Manufacturing
- Predictive maintenance on Soliq: catch failures 2–3 weeks early
- Quality defect detection at line speed
- Production scheduling optimisation
Financial Services
- Document AI for KYC, loan applications, contract review
- Fraud pattern detection on transaction streams
- AI-assisted compliance monitoring
Healthcare
- Clinical records extraction and structuring
- Diagnostic decision support
- Patient communication AI with ABDM-aware design
D2C & E-Commerce
- AI support with 89% resolution rate (vs 41% before)
- Personalised recommendations on purchase history
- Demand forecasting and replenishment
From first call to production AI in four steps.
Discovery call
We understand your use case, data, constraints, and what success looks like. One call, 30–45 minutes.
Scoped POC
A working prototype in 2–3 weeks against your real data. You see quality before committing to a full build.
Production build
Full pipeline: model selection, grounding, agent logic, guardrails, API integration, frontend if needed.
Evals & monitoring
Every capability ships with a graded eval suite. Nightly runs on production traffic. Drift alerts. Nothing degrades silently.
Questions about AI development.
Calling one model API gives you a single model with no routing, no grounding, no evals and no audit trail. We build the orchestration layer above the foundation models (OpenAI, Anthropic, Google, Meta, private models) and add everything you need to ship safely in production: model routing, retrieval, agents, guardrails, governance and observability. The right model for your task is selected per call. You're never locked into one provider.
Yes. We support on-premise deployment, private cloud VPCs, and dedicated endpoints (AWS Bedrock, Azure OpenAI, Vertex AI). We sign mutual NDAs and DPAs before any sensitive data flows. We never use customer data to train shared models.
That's the most common situation we walk into. Demo failure usually means missing grounding (the model answered from its training, not your data), no evals (quality was never measured), or no guardrails (edge cases weren't handled). We start with a diagnostic sprint to identify where the previous system broke, then rebuild the right way.
You swap it in. Every system we build is model-agnostic by design: the orchestration layer routes to whichever model wins your evals, and the application layer never knows which one ran. Migrations that take other teams months are a configuration change for ours.
Integrate, almost always. Most projects start by adding AI capabilities to systems you already run (ERPs, CRMs, support stacks, BI tools, internal apps) through APIs, SDKs or embedded widgets. A full rebuild only makes sense when the underlying system is the bottleneck.
Describe your AI use case.
Tell us what you're trying to do: the use case, the data you have, and what "working" looks like. We'll come back with a clear next step, usually a scoped POC proposal.