Fragile AI Demos
Basic prompt templates with no memory, unconstrained text outputs, frequent hallucinations, zero audit logging, and security vulnerabilities that leak proprietary data.
Building reliable, enterprise-grade AI agents requires more than prompt templates. Discover our deterministic 10-step lifecycle — from business workflow mapping and private RAG ingestion to multi-agent swarms, safety guardrails, and real-time observability.
We analyze your existing manual business workflows, identify high-volume bottleneck tasks, calculate expected token economics, and define clear success criteria. We document deterministic boundaries so the agent knows precisely what it can and cannot execute.
We select the appropriate agent topology: single specialized agent, hierarchical supervisor-worker graph, or collaborative multi-agent swarm. We map out state schemas, memory buffers, decision nodes, and escalation fail-safes.
We clean, parse, and chunk your proprietary enterprise knowledge (PDF manuals, SOPs, past CRM conversations, catalog databases). Text is embedded using state-of-the-art embedding models into pgvector or Pinecone with hybrid BM25 + dense semantic retrieval.
We benchmark Google Gemini 1.5/2.0, Claude 3.5 Sonnet, GPT-4o, and on-premise open-weights models (Llama 3.3 / DeepSeek) against your specific domain prompts. We optimize for latency, context window utilization, reasoning depth, and cost efficiency.
We build type-safe Pydantic tool interfaces that allow the agent to safely interact with your actual business software: querying SQL databases, reading/writing CRM records, scheduling Google Calendar appointments, and sending WhatsApp messages.
We implement dual-tier memory: short-term sliding conversational window for active dialogue and long-term episodic vector memory for returning users. Redis state stores guarantee zero context loss across multi-turn sessions.
We deploy programmatic guardrails (NeMo Guardrails, regex pre-filters, PII redaction) to completely block prompt injection, jailbreaks, and out-of-domain drift. Responses must validate against strict JSON schemas before being executed or returned to users.
For complex swarms, we wire stateful LangGraph or CrewAI graphs. A supervisor agent breaks down user goals, delegates subtasks to specialized worker agents (Researcher, Coder, Validator), and synthesizes validated outputs with self-correction loops.
We run synthetic adversarial test suites and LLM-as-a-judge evaluations across hundreds of edge-case scenarios. We stress-test for factual accuracy, refusal compliance on out-of-scope prompts, and speed under peak concurrent queries.
We deploy the agent containerized on Google Cloud Run, AWS ECS, or your private on-premise infrastructure. We instrument full Langfuse telemetry to monitor token latencies, step-by-step traces, costs, and user satisfaction ratings in real time.
The difference between an unreliable demo and a production system that generates measurable revenue.
Basic prompt templates with no memory, unconstrained text outputs, frequent hallucinations, zero audit logging, and security vulnerabilities that leak proprietary data.
Deterministic state graphs, verified vector RAG, strict Pydantic JSON schemas, automated PII masking, human-in-the-loop gates, and sub-second execution.
Up to 80% reduction in customer response times, automated lead capture 24/7, zero manual data entry errors, and rapid payback starting from ₹1,50,000.