Fig 2.0Services · AI development
AI developmentAI features that survive contact with real users.
Most AI demos work. Most AI products do not, because nobody measured them. We build agents, retrieval systems and LLM-powered features with an evaluation harness from day one, guardrails that are tested rather than hoped for, and cost controls that keep the finance team calm.
Engagement models
- Fixed-scope build (6–10 weeks)
- Dedicated AI squad
- Advisory and architecture review
Fig 2.1What you get
What you walk away with.
- 01
Agents and workflows
Single agents and multi-agent systems with tool use, memory and human approval gates where they matter.
- 02
Retrieval (RAG)
Hybrid search over your documents and data, with citations, chunking that respects your document structure and freshness you can see.
- 03
Evaluation harness
A test set built from your real cases, scored automatically on every change. Accuracy, citation precision, refusal rate, latency, cost.
- 04
Guardrails
Input and output checks, PII handling, prompt-injection defence, and a kill switch per feature.
- 05
Integration
Into your product, your ticketing tool, Teams or WhatsApp. The agent meets people where they already work.
- 06
Operations
Monitoring, tracing, cost dashboards and model-upgrade playbooks so the feature keeps working as models change.
Fig 2.2Proof
Where we have done this.
- 01
Our own TriageIQ, PolicyDesk and AskCXO run on the same platform we build for clients.
- 02
Every project ships with an evaluation report alongside the demo.
- 03
Hindi and English handled as first-class languages, not an afterthought.
Fig 2.3Stack
Tools we reach for.
Chosen per project on fit and cost, never on habit. We will tell you when something simpler will do.
- Claude, GPT and open-weight models
- Python, TypeScript
- Vector and hybrid search
- LangGraph-style orchestration or plain code
- OpenTelemetry tracing
- AWS, Oracle Cloud, on-prem
Fig 2.4Questions
Asked often.
Which model should we use?
Usually more than one. We pick per task on measured accuracy and cost, and we design so you can switch when a better or cheaper model arrives.
Can it run inside our network?
Yes. Open-weight models in your VPC or on-prem, with the same eval harness.
How do you stop hallucinations?
Retrieval with citations, constrained outputs, verification steps and refusal when confidence is low, measured on your own cases before and after every change.
Fig 2.5Related products
Built with this capability.
Fig 2.6More services
Also on offer.
Have a product in mind?
Let's build it.
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