Production AI systems, engineered to be yours.
We design, build, and deploy AI systems that organisations fully own and operate — AI agents, RAG platforms, voice interfaces, and private LLM deployments. Open-source-first. Transparent architecture. No vendor lock-in.
How we work
Engineering, not consulting.
Most AI work in the market is advice — strategy decks, vendor selections, prompt frameworks. Useful, but it doesn't ship systems.
ClearedCircle is an engineering team. We design, build, and deploy production AI infrastructure end-to-end — the same discipline we apply to our own MITRA products, applied to your operations. The deliverable is a system that runs, not a roadmap.
The same memory-native architecture that underlies our products is available as a foundation for what we build with you.
What we build
Five practice areas. One engineering team.
01
AI Agents & Workflow Orchestration
Autonomous agents that pursue objectives, maintain state, and coordinate across tools and systems. Built with LangGraph, the Model Context Protocol, and custom orchestration layers when off-the-shelf agent frameworks fall short.
- LangGraph multi-agent systems
- MCP servers and tooling
- Long-running workflow agents
- Human-in-the-loop review surfaces
02
Retrieval-Augmented Generation (RAG)
Private knowledge systems that answer from your data, not the public internet. Document intelligence, semantic search, hybrid retrieval, and contextual ranking — engineered for accuracy under enterprise-grade evaluation.
- Document intelligence pipelines
- Vector + lexical hybrid retrieval
- Evaluation harnesses (RAGAS, custom)
- Reranker and query-expansion strategies
03
Voice AI & Conversational Interfaces
Speech-to-text, text-to-speech, and full voice-loop systems — including vernacular Indian-language deployments at production scale. The same voice infrastructure that powers ShopMITRA is available for client engagements.
- Multilingual STT and TTS
- Real-time voice agents
- Telephony-integrated voice AI
- On-device and edge voice processing
04
Private & On-Premise LLM Deployments
Open-source LLMs deployed inside your infrastructure or air-gapped environments — Llama, Qwen, Mistral, DeepSeek. Inference stacks, GPU orchestration, and model-serving optimised for your latency and cost envelope.
- vLLM, TGI, and Ollama deployments
- GPU orchestration on Kubernetes
- Air-gapped and sovereign installations
- Model evaluation and selection
05
AI Product Engineering
End-to-end build of AI-native products: architecture, backend services, web and mobile clients, observability, and security. The same engineering discipline behind the MITRA Series, available as a service.
- Python, FastAPI, Flutter, React/Next.js
- Supabase, PostgreSQL, Redis, vector DBs
- Observability and evaluation infrastructure
- Security, compliance, and audit logging
How to engage
Three engagement shapes.
Scoped projects, ongoing retainers, or framework licensing — chosen to match what the work actually is, not to fit a procurement template.
Project
Scoped engagements with defined deliverables and timelines. Typical for proofs-of-concept, AI feature builds, and integration work.
Timeline
4–16 weeks
Deliverable
Production system + handoff
Retainer
Ongoing engineering capacity for organisations evolving AI systems over time. Includes managed services, evaluation, and continuous improvement.
Timeline
Monthly · 6+ months
Deliverable
Dedicated engineering capacity
Platform licence
License the ClearedCircle MITRA framework for internal deployment — across departments, subsidiaries, or client sites. Includes enablement and ongoing support.
Timeline
Annual
Deliverable
Framework + enablement
How we work
Four principles, applied to every engagement.
You own what we build.
Code, models, weights, infrastructure — at the end of an engagement, the client owns and controls the deployed system. No vendor lock-in. No hidden dependencies.
Open-source first, where it matters.
For deployments where data sovereignty, cost predictability, or independence from frontier-lab pricing matters, we lead with open-source LLMs. For deployments where capability matters more, we use the best tool for the job.
Transparent architecture.
Every system we deliver comes with the architecture diagrams, deployment runbooks, and evaluation harnesses needed for someone else to operate it. No black boxes handed off.
Evaluation before scale.
Every AI system is measurable. We build evaluation harnesses alongside the system itself, so claims about accuracy, latency, and cost are grounded in numbers — not impressions.
What we don't do
What we say no to.
Boutique engineering means choosing the work carefully. A few engagement shapes we don't take, and the reasons.
We don't deliver generic offshore body-shops. We can scale teams when an engagement warrants it, but the work is led and delivered by us, not by N engineers handed over to a client manager.
We don't take fixed-price RFPs for AI work where the requirements are ambiguous. AI engagements need discovery before pricing — we'll do paid scoping if needed, not blind bids.
We don't take engagements where the client doesn't want to own the output. If you want a vendor who keeps you locked into them, we're the wrong team.
Why ClearedCircle
An engineering team that ships its own products.
Most AI services firms have never built a product of their own. They architect for clients, not for themselves. ClearedCircle is different: we build and operate our own MITRA Series products, on the same infrastructure we deploy for client engagements. The discipline shows up in the work.
We are headquartered in Navi Mumbai, India, with engagement coverage across IST, CET, and EST time zones. ClearedCircle TechPlatforms Private Limited is DPIIT-recognised and MSME-registered, with the governance and compliance posture that enterprise procurement teams expect.
We're small on purpose. The engagements we take are led and delivered by the same team that built our products.
Have an engagement in mind?
Tell us what you're building, what stage you're at, and what's blocking you. We respond to every serious inquiry within two business days.