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.
From single-agent tools to multi-agent systems with memory and guardrails — agents that survive real users.
Agents with structured tool access — call APIs, query databases, run code, execute transactions — with retry logic, cost guardrails, and audit logs.
Specialised agents working together — planner, researcher, executor, critic — coordinated via LangGraph / CrewAI / AutoGen with shared memory.
Ground LLMs in your private knowledge — documents, databases, APIs — with hybrid search, re-ranking, citations, and permission-aware retrieval.
Long-running, multi-step workflows with branching, loops, human-in-the-loop checkpoints, and durable state — via LangGraph or Temporal.
Long-term memory layers — episodic, semantic, working — that let agents remember users, preferences, prior conversations, and prior decisions.
Production agents need production observability — every step traced, eval'd, cost-tracked. We deploy Langfuse, LangSmith, Helicone with custom dashboards.
Production agents need production guardrails — input/output filtering, jailbreak detection, PII redaction, cost limits, action confirmation.
Sometimes off-the-shelf LangChain isn't right — we build custom orchestration in Python / TypeScript when production needs demand it.
Single agent with tools + RAG + observability — production-grade — live in 4–5 weeks.
Coordinated multi-agent workflow on LangGraph / CrewAI — with memory, evals, guardrails, ops dashboard.
Ongoing platform — we run the agents, tune prompts, upgrade models, add capabilities monthly.
Define agent purpose, tools, memory needs, success metric. Architect single-agent vs multi-agent, LangGraph vs CrewAI vs custom — based on real complexity, not hype.
Build the RAG pipeline, tool wrappers, eval set. Test retrieval accuracy and tool reliability before any agent reasoning is layered on top.
Iterative agent build with eval-driven development — every prompt change goes through the eval harness. Cost tracking and observability from day one.
Soft launch with a closed cohort. Measure task success rate, cost per task, satisfaction. Tune prompts, tool design, fallback paths.
Production launch with full observability. Monthly retainer: regression evals on every prompt change, model upgrade testing, new capability rollout.
LangChain, LangGraph, CrewAI, AutoGen, Pydantic AI, or custom Python — we pick based on real complexity and team comfort, not framework hype.
Every prompt change goes through an eval harness. We catch regressions before they ship. Numbers, not vibes — task success, accuracy, cost per task.
Input filtering, output validation, jailbreak detection, PII redaction, cost limits, action confirmation. Production AI agents need production safety.
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.
Model routing, caching, prompt compression, embedding selection. Most agents we audit can be made 40–60% cheaper without accuracy loss.
Source in your GitHub, infra in your cloud, prompts in your repo, evals in your account. Switch vendors? It all comes with you.
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.
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'.
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.
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).
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.
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.
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.
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.
Odoo 20 AI runs on IAP credits at €1 each. See which AI features use credits, what OCR, SMS and contact enrichment cost, and a sample monthly budget.
Free 30-minute architecture audit. We'll design the agent shape and send a fixed-price proposal within 48 hours.
