AI agents that do the work — with tools, memory, and guardrails.
Production multi-agent systems on LangChain, LangGraph, CrewAI, and AutoGen — grounded in your data via RAG, observed with Langfuse/LangSmith, deployed on your infra.
Tool-using agents. Multi-agent coordination. Stateful workflows. Eval-driven development. Cost guardrails. Production safety from day one.
Eight agent capabilities we ship to production
From single-agent tools to multi-agent systems with memory and guardrails — agents that survive real users.
Tool-using Agents
Agents with structured tool access — call APIs, query databases, run code, execute transactions — with retry logic, cost guardrails, and audit logs.
Multi-agent Systems
Specialised agents working together — planner, researcher, executor, critic — coordinated via LangGraph / CrewAI / AutoGen with shared memory.
RAG Systems (Retrieval Augmented)
Ground LLMs in your private knowledge — documents, databases, APIs — with hybrid search, re-ranking, citations, and permission-aware retrieval.
Stateful Workflow Agents (LangGraph)
Long-running, multi-step workflows with branching, loops, human-in-the-loop checkpoints, and durable state — via LangGraph or Temporal.
Agent Memory & Personalisation
Long-term memory layers — episodic, semantic, working — that let agents remember users, preferences, prior conversations, and prior decisions.
Agent Observability & Eval
Production agents need production observability — every step traced, eval'd, cost-tracked. We deploy Langfuse, LangSmith, Helicone with custom dashboards.
Agent Safety & Guardrails
Production agents need production guardrails — input/output filtering, jailbreak detection, PII redaction, cost limits, action confirmation.
Custom Agent Frameworks
Sometimes off-the-shelf LangChain isn't right — we build custom orchestration in Python / TypeScript when production needs demand it.
The agent + RAG stack we build on
Agent frameworks
LLMs
Vector DBs
Embeddings
Orchestration
Observability
Eval
Hosting
Work with us the way that fits your business
Agent Pilot
Single agent with tools + RAG + observability — production-grade — live in 4–5 weeks.
- 1 agent + tools
- RAG over 1–2 sources
- Observability + evals
- Cost guardrails
- 30-day support
Multi-agent System
Coordinated multi-agent workflow on LangGraph / CrewAI — with memory, evals, guardrails, ops dashboard.
- Planner + executor + critic
- Multi-source RAG
- LangGraph orchestration
- Cost + safety guardrails
- Ops dashboard
- 3-month optimisation
Managed Agent Platform
Ongoing platform — we run the agents, tune prompts, upgrade models, add capabilities monthly.
- Monthly capability additions
- Model upgrades
- Cost optimisation
- QBR with KPIs
- SLA-backed support
From use-case design to live agent in seven weeks
Use-case & Architecture Design
Week 1Define agent purpose, tools, memory needs, success metric. Architect single-agent vs multi-agent, LangGraph vs CrewAI vs custom — based on real complexity, not hype.
Knowledge & Tool Layer
Week 2Build the RAG pipeline, tool wrappers, eval set. Test retrieval accuracy and tool reliability before any agent reasoning is layered on top.
Agent Build & Eval
Weeks 2–5Iterative agent build with eval-driven development — every prompt change goes through the eval harness. Cost tracking and observability from day one.
Pilot with Real Users
Week 5–6Soft launch with a closed cohort. Measure task success rate, cost per task, satisfaction. Tune prompts, tool design, fallback paths.
Launch & Operate
Week 7 onwardsProduction launch with full observability. Monthly retainer: regression evals on every prompt change, model upgrade testing, new capability rollout.
Why engineering teams trust us with agent development
Framework-agnostic
LangChain, LangGraph, CrewAI, AutoGen, Pydantic AI, or custom Python — we pick based on real complexity and team comfort, not framework hype.
Eval-driven development
Every prompt change goes through an eval harness. We catch regressions before they ship. Numbers, not vibes — task success, accuracy, cost per task.
Production guardrails
Input filtering, output validation, jailbreak detection, PII redaction, cost limits, action confirmation. Production AI agents need production safety.
Vector DB experts
We've deployed Pinecone, Qdrant, Weaviate, ChromaDB, and pgvector at scale. We know which one fits your workload and how to keep latency / cost sane.
Cost-engineered
Model routing, caching, prompt compression, embedding selection. Most agents we audit can be made 40–60% cheaper without accuracy loss.
Owned by you
Source in your GitHub, infra in your cloud, prompts in your repo, evals in your account. Switch vendors? It all comes with you.
AI agents for every industry
Frequently asked questions
LangChain vs LangGraph vs CrewAI vs AutoGen — which is right?
Depends on complexity. LangChain: single agents with tools, RAG, sequential chains. LangGraph: complex stateful workflows with branching, loops, human-in-the-loop — most production multi-step agents. CrewAI: simple role-based multi-agent (researcher + writer + editor). AutoGen: research-grade multi-agent experiments. We pick per use case, not preference.
What's the difference between an AI chatbot and an AI agent?
A chatbot answers questions. An agent takes actions — calls APIs, queries databases, executes transactions, navigates multi-step workflows, with memory across the conversation. Agents are what you need when the work isn't just 'what's the answer' but 'do the thing'.
Is RAG still relevant with long-context models (GPT-4 1M, Claude 200K)?
Yes — for three reasons: (1) cost — RAG is 100x cheaper than dumping a corpus into the context window; (2) latency — small context wins for time-to-first-token; (3) freshness — RAG indexes update without retraining. Long-context is great for in-conversation reference, RAG is for grounded knowledge access at scale.
What does it cost to build an AI agent?
Pilot single agent: ₹3L–₹8L ($4K–$10K). Multi-agent system: ₹8L–₹30L ($10K–$36K). Per-task running cost depends on model + tools — typical production agents run ₹0.5–₹10 ($0.006–$0.12) per task after our cost-optimisation pass. Managed retainer: ₹50K–₹4L/month ($600–$5K).
How long does it take to build a production AI agent?
Single agent with RAG + tools + observability: 4–5 weeks. Multi-agent system with planner/executor/critic + multi-source RAG + ops dashboard: 6–10 weeks. Custom multi-modal agents (text + voice + image): 10–14 weeks.
Which vector database should we use?
Pinecone for managed simplicity at scale. Qdrant for self-hosted or hybrid. Weaviate for built-in hybrid + multi-tenant. pgvector if you're already on Postgres and your scale is modest (<10M vectors). ChromaDB for prototype / single-tenant. We benchmark and pick per workload.
How do you handle agent hallucination and bad tool calls?
Structured output (JSON-schema-validated tool calls), retrieval grounding for facts, eval harness on every prompt change, confidence-based escalation, human-in-the-loop checkpoints for high-stakes actions, observability via Langfuse/LangSmith to spot drift in production.
Can agents be deployed on-prem / regulated infra?
Yes. Local Llama / Mistral / Phi on customer infrastructure, Pinecone-self-hosted or Qdrant on-prem, full air-gapped deployments. We've done this for BFSI, healthcare, and government clients. Performance is a trade-off but accuracy gap to GPT-4o has narrowed significantly.
Ready to build production AI agents?
Free 30-minute architecture audit. We'll design the agent shape and send a fixed-price proposal within 48 hours.