Simple chatbots answer questions; autonomous agents execute complex multi-step jobs. We build production-ready agentic AI systems that reason, call external APIs, query databases, and execute workflows with deterministic safety guardrails.
LangGraph & CrewAI Orchestration • Deterministic Tool Calling • Full Audit Logging
Groooh engineers custom autonomous AI agents and multi-agent workflows using LangGraph, LangChain, CrewAI, and modern LLMs (Claude 3.5 Sonnet, GPT-4o). Deliverables include tool integrations, vector database RAG pipelines, human-in-the-loop approval gates, and observability, shipping in 2 to 6 weeks at ~20% below standard agency rates.
Multi-Agent Systems
Scope
Architecting collaborative multi-agent teams using LangGraph and CrewAI: where specialized agents (Researcher, Writer, Reviewer, Executor) divide and execute complex tasks.
Best For
Complex workflows that require multiple stages of analysis, fact-checking, and execution.
Tool Calling
Scope
Equipping AI agents with structured function-calling tools to query internal SQL databases, update CRMs, process payments, and trigger webhooks reliably.
Best For
Replacing repetitive manual data entry, customer support escalation, and operational tasks.
Enterprise RAG
Scope
Building hybrid vector search (Pinecone, pgvector) with semantic re-ranking, document chunking, contextual compression, and automated source citations.
Best For
Grounding agents in proprietary internal knowledge bases with zero hallucination risk.
Safety Guardrails
Scope
Implementing NeMo Guardrails, automated PII redaction, confidence threshold triggers, and mandatory human sign-off modals for irreversible actions.
Best For
Regulated enterprises (Finance, Legal, Healthcare) that require strict governance and audit compliance.
AI Automation
Scope
Building end-to-end background agents that process invoices, triage incoming emails, generate customized sales proposals, and reconcile reports 24/7.
Best For
Slashing hundreds of hours of manual administrative labor across operations teams.
LLM Observability
Scope
Integrating Langfuse, LangSmith, or Arize Phoenix for token usage tracking, latency profiling, automated regression evals, and prompt caching.
Best For
Preventing token budget overruns and monitoring model output quality in production.
Simple chatbots answer questions; autonomous agents execute complex multi-step jobs. We build production-ready agentic AI systems that reason, call external APIs, query databases, and execute workflows with deterministic safety guardrails.
We don't let LLMs run wild. We bind agents to strict schema validations (Pydantic/Zod) and state machine graphs where outputs must pass verification before executing.
Agents can draft, research, and propose autonomously. But sending an invoice, charging a card, or updating clinical records always requires human sign-off.
Every LLM thought process, tool execution parameter, input prompt, and output token is permanently logged with timestamps for complete audit compliance.
Mapping the target workflow, defining permitted tools, safety constraints, and success evaluation metrics.
Building the LangGraph state machine, tool integration contracts, and vector indexing pipeline.
Implementing human-in-the-loop approval interfaces, streaming response states, and automated evaluation tests.
Adversarial prompt injection testing, Langfuse observability integration, containerized deployment, and handoff.
We leverage proven agent orchestration frameworks to deliver production-grade AI agents in 2 to 6 weeks at ~20% below standard AI consultancy rates:
Single-Purpose AI Agent Proof-of-Concept (1 to 2 weeks): $4,500 to $8,500 (single autonomous agent, 2 tool integrations, structured output validation)
Production Multi-Agent System with Tool Calling & RAG (3 to 6 weeks): $18,000 to $38,000 (multi-agent orchestration, LangGraph state machine, vector database, human approval UI)
Enterprise Agentic AI Platform with Guardrails & Observability (6 to 10 weeks): $38,000 to $78,000 (enterprise ERP/CRM integrations, strict compliance guardrails, automated evals)
Includes full Python/TypeScript agent codebase, Docker configurations, and automated prompt evaluation suites.
| Capability | Standard / Stack |
|---|---|
Agent Orchestration | LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex |
Foundation Models | Anthropic Claude 3.5 Sonnet, OpenAI GPT-4o, Google Gemini 1.5 Pro, Llama 3.3 |
Vector Databases | Pinecone, pgvector (PostgreSQL), Qdrant, Weaviate |
Observability & Evals | Langfuse, LangSmith, Arize Phoenix, Ragas automated evals |
Safety & Validation | Pydantic, Instructor, NeMo Guardrails, JSON Schema enforcement |
A chatbot simply answers text questions based on conversational memory. An agentic AI system has agency: it can break goals into multi-step execution plans, call external APIs, query live databases, evaluate its own intermediate work, and take concrete actions autonomously.
Our rates run ~20% below traditional AI consultancies. A targeted single-agent proof of concept costs $4,500 to $8,500 (1 to 2 weeks); a full production multi-agent system with tool calling costs $18,000 to $38,000 (3 to 6 weeks). Quotes are fixed-scope.
We use deterministic state graphs (LangGraph) rather than open-ended autonomy. Agents are restricted to strict Pydantic/Zod schemas, tool inputs must match exact type definitions, and high-impact actions require human-in-the-loop confirmation.
Yes. We build custom API tools allowing agents to read and write data to PostgreSQL, MongoDB, Salesforce, HubSpot, Stripe, Slack, and internal custom REST/GraphQL endpoints.
We select the optimal model for each specific subtask: Anthropic Claude 3.5 Sonnet for complex coding and reasoning, GPT-4o for fast tool calling, and lightweight open-source models (Llama 3) for high-volume background tasks.
A focused single-agent tool takes 2 to 3 weeks. A robust multi-agent enterprise system takes 4 to 6 weeks. We provide live staging access for testing throughout.
We implement semantic prompt caching, prompt token compression, and smart model routing (routing simple tasks to cheaper mini models), reducing ongoing API costs by up to 60%.
Human-in-the-loop is an architectural pattern where the AI prepares a complex action (like drafting a legal agreement or issuing a refund) and presents a visual diff for an authorized human to approve with one click before execution.
Yes. Every production agent includes integration with Langfuse or LangSmith, providing real-time traces, token latency metrics, user feedback tracking, and error alerts.
You own 100% of the custom agent codebase, prompt templates, vector database embeddings, and API connection logic from day one.
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