Agents that read, check and act on the documents your business runs on.
From the first readiness assessment to agents in production and the MLOps that keeps them honest. We design, build and run AI that sits inside your workflows — contracts, invoices, financial statements, policies — with audit trails your compliance team will sign off.
LLMs, AI agents, model deployment, RAG and generative AI
AI advisory & solution design
Readiness and data-maturity review, use-case discovery, responsible-AI strategy, open-source versus proprietary model selection.
Custom AI agent development
Multi-agent coordination, goal-oriented agents and autonomous task chaining with LangGraph, integrated with your APIs, CRMs and tools.
Contract & compliance intelligence
Clause detection, risk scoring, playbook deviation, invoice-to-contract validation against GST, TDS and internal policy.
Data & document AI
OCR, LayoutLM and table extraction across PDF, Word, Excel and scans; retrieval-augmented Q&A over your own documents.
Collaboration & communication AI
Knowledge-base agents, email summarisation and assistants for Microsoft Teams, Slack and Gmail.
AI managed services & MLOps
Drift and hallucination monitoring, retraining pipelines, model governance and SLA-based AI support.
AI managed services, end to end
Assessment & design
- Evaluate AI readiness and data maturity
- Define use cases and a responsible-AI strategy
- Model selection: open source or proprietary
Model integration & deployment
- Deploy LLMs or custom-trained models
- Fine-tuning and prompt engineering
- API integrations: OpenAI, Claude, internal tools
Monitoring & support
- Model performance tracking: drift, hallucination
- Continuous optimisation of responses
- Incident response and SLA-based support
MLOps / AIOps enablement
- CI/CD for the model lifecycle
- Agent orchestration (LangGraph, AutoGPT)
- Containerised AI workflows on Docker and Kubernetes
How an AI assessment runs
Assessment & identification
- Business goals and challenges for AI
- Data architecture and ML readiness
- Stakeholder interviews and use-case discovery
- Initial feasibility and ROI
Gap analysis & prioritisation
- Gaps in data, tools and infrastructure
- Team AI-skill readiness
- High-impact, quick-win use cases
- Labelled data and governance gaps
Architecture & solution strategy
- AI blueprint: model, pipeline, deployment
- Frameworks and platforms
- Security, monitoring and explainability
- Build-versus-buy recommendation
Business case & governance
- TCO and ROI estimate
- Risk mitigation and audit mechanisms
- AI ethics, privacy and compliance policies
- Phased roadmap and change management
What you can count on
- Cost-effective AI model operations
- High availability of AI APIs and services
- Automated pipeline management
- Secure, governed model delivery
- 24×7 monitoring and bias mitigation
- ITIL-aligned AI service governance
Delivery models
| Model | What we run | Platforms |
|---|---|---|
| SaaS | AI copilots, chatbots, analytics dashboards | ChatGPT · Jasper · Notion AI |
| PaaS | LLM APIs, vector databases, model fine-tuning | OpenAI · Cohere · Hugging Face · Pinecone |
| Hybrid | On-premises and cloud AI model operations | NVIDIA DGX · Azure · AWS |
Platforms & tools
AI & Automation in practice
VerifiableContract: an AI agent for the whole contract lifecycle
DocuMindz: financial document review in a fraction of the time
Observer.AI: contract and invoice compliance, checked as documents arrive
Tell us what you need to build, move or run.
An engineer reads every enquiry, not a sales script. We come back with an approach, a team and a plan you can hold us to.