Customer Support AI Agents
Tier-1 deflection agents grounded in your knowledge base, ticketing system, and policies — with human handoff, sentiment alerts, and conversation analytics for QA.
AI Agent Development
Production-Grade AI Agents Grounded in Your Data
We build production-grade AI agents — retrieval-augmented assistants, tool-calling workflow agents, evaluation harnesses, and bespoke LLM applications — for enterprises that want measurable AI leverage without leaking data, hallucinating in customer chats, or burning runway in opaque token spend.
RAG-grounded · Eval-tested · VPC / on-prem ready · No training on your data
5+LLM providers integrated
RAGGrounded in your data
100%Eval-tested before launch
VPCOn-prem ready
AI Agents We Build
From customer-facing assistants to internal workflow automation — agents grounded in your data, evaluated against ground truth, and deployed with full observability.
Internal Knowledge Assistants
Employee-facing assistants that answer policy, finance, HR, and ops questions from your internal docs — with role-based redaction so confidential data stays scoped.
Workflow Automation Agents
Tool-calling agents that read tickets, update CRMs, generate documents, fire approvals, and orchestrate multi-step business workflows — with full audit trails.
Document & Contract Agents
Extract, summarise, and compare contracts, RFPs, invoices, and reports — with citation back to the source span so legal and finance teams can verify every claim.
Analytics & BI Agents
Natural-language interfaces over your data warehouse — translating questions into safe SQL, charts, and narratives with guardrails against accidental query-bombs.
Custom GPTs & Co-Pilots
Domain-specific co-pilots embedded inside your existing products — sales co-pilots, dev assistants, design helpers, and operations bots wired into your real tools.
Industries We Serve
AI agents engineered for knowledge-intensive industries where data residency, accuracy, and regulatory comfort all matter.
FinTech & BFSI
Healthcare
Retail & D2C
EdTech
Logistics
Manufacturing
Real Estate
Professional Services
AI Stack & Models
Multi-provider LLM gateway, modern RAG frameworks, and vector databases — chosen per cost, latency, and data-residency posture.
OpenAI GPT-4oLLM
Anthropic ClaudeLLM
Google GeminiLLM
Llama 3Open-Source LLM
LangChainFramework
LlamaIndexFramework
LangGraphAgent Orchestration
PineconeVector DB
WeaviateVector DB
pgvectorVector DB
OpenAI EmbeddingsEmbeddings
Sentence TransformersEmbeddings
Our AI Agent Development Process
A six-step rhythm engineered so AI features land in production with measurable performance — not vibes.
Use-Case Scoping
We define the agent's job, success metrics, data sources, tool permissions, and red-team criteria — before any model is selected. Use-cases without measurable ROI get filtered out.
Data & Knowledge Pipeline
We ingest, chunk, embed, and index your documents, databases, and APIs into a vector store with metadata filters — building the RAG foundation your agent will retrieve from.
Agent & Prompt Engineering
System prompts, few-shot examples, tool definitions, and orchestration logic — built iteratively with versioned prompts and A/B testing across multiple LLM providers.
Evaluation & Red-Teaming
We build an eval harness with ground-truth datasets, LLM-as-judge scoring, hallucination detection, jailbreak resistance tests, and PII leakage checks — your safety net before launch.
Production Deployment
Cost-tracked LLM gateway, caching, rate limiting, observability, prompt logging with redaction, and VPC/on-prem deployment when residency matters.
Continuous Improvement
User feedback collection, prompt versioning, model upgrades (GPT-4o to GPT-5, Claude 3.5 to 4), eval-set growth, and quarterly cost optimisation reviews.
Why Choose Hendoi for AI Agents
Six commitments that separate AI agents that ship and perform from AI agents that demo well and embarrass you in production.
Data Stays Yours
VPC and on-prem deployments where residency matters. Prompt logs scrubbed of PII. Vector stores in your cloud. We do not train on your data — full stop.
Eval-Tested, Not Demo-Tested
Every agent ships with an evaluation harness — ground-truth datasets, LLM-as-judge scoring, and red-team tests. You see real performance numbers, not a curated demo.
Token Economics Transparent
We show you cost per conversation, per task, and per user — with caching, model routing (cheap models for easy queries, premium models for hard ones), and burn dashboards.
Multi-Provider, Not Locked-In
We build behind an LLM gateway so you can swap OpenAI for Anthropic, or self-hosted Llama, in hours — not weeks. No vendor lock-in by design.
Senior Engineering, Not Hype
Architecture is reviewed by Sundarapandi Muthupandi (CEO). We say no to use-cases that AI cannot reliably solve — saving you from launching agents that hurt your brand.
Transparent Engagement
Weekly demos with eval-set results, direct access to engineers, signed SOWs with assumptions documented, and predictable burn — even on speculative AI work.
Engagement Models
Engagement shapes that match where your AI initiative actually is — exploring, shipping, or scaling.
Discovery & Pilot Sprint
A 4-6 week sprint to validate whether AI is the right tool for your problem — with a working prototype, eval results, and a go/no-go recommendation at the end.
- Fixed-scope pilot
- Working prototype + eval results
- Honest go/no-go recommendation
Production AI Squad
A dedicated AI squad — LLM engineer, backend engineer, evals engineer — building, deploying, and iterating on your agent in production.
- Senior LLM + backend engineers
- Evals + observability built in
- Weekly demos & cost reviews
AI Retainer & Tuning
A monthly retainer covering prompt tuning, model upgrades, eval-set growth, cost optimisation, and incident response for AI features in production.
- Prompt + model upgrades
- Eval-set growth
- Cost optimisation reviews
Real-World Use Cases
Representative AI agents we have engineered across BFSI, healthcare, retail, sales, manufacturing, and legal.
NBFC Loan-Officer Assistant
Internal assistant for NBFC loan officers — answers policy questions, drafts customer letters, summarises bureau reports, and flags missing KYC documents — grounded in the NBFC's own policy library.
Hospital Discharge Summary Agent
Agent that drafts discharge summaries from clinician notes and EMR data, with citation to source records — clinicians review and approve, cutting documentation time by 60%.
D2C Customer Support Co-Pilot
WhatsApp + web chat agent for a D2C brand — handles order status, returns, sizing, and product questions with seamless handoff to humans for complex cases.
Sales Enablement Agent
Sales co-pilot integrated with Salesforce and Gong — generates outreach drafts, summarises calls, suggests follow-ups, and updates CRM fields with one-click approvals.
Manufacturing Maintenance Agent
Agent for plant maintenance engineers — answers troubleshooting questions from equipment manuals, finds historical incidents, and drafts work orders for review.
Legal Contract Analysis Agent
Agent that extracts key clauses, identifies non-standard terms, and compares contracts against your playbook — with citation to the exact span for every flagged item.
Frequently Asked Questions
Common questions enterprise buyers ask before committing to an AI agent programme.
What is an AI agent and how is it different from a chatbot?
A chatbot answers questions. An AI agent reads, reasons, and acts — it can call tools (search your database, send emails, update CRMs, generate documents), make multi-step decisions, and complete tasks autonomously with appropriate guardrails.
Which LLM providers do you work with?
OpenAI (GPT-4o, GPT-5), Anthropic (Claude 3.5/4), Google (Gemini), Mistral, Cohere, and open-source models (Llama 3, Qwen, DeepSeek) self-hosted on your infrastructure when residency or cost requires it.
Will my company's data be used to train the model?
No. We use enterprise/API tiers (OpenAI Enterprise, Claude for Business) that explicitly do not train on your data. For maximum control, we deploy open-source models in your own VPC or on-prem environment.
How do you prevent hallucinations and wrong answers?
Retrieval-augmented generation (RAG) grounds responses in your actual documents. We add citation requirements, refuse-when-unsure prompts, an eval harness that catches hallucinations on a known dataset, and human-in-the-loop for high-stakes decisions.
How much does an AI agent cost to build and operate?
Build cost depends on scope and integrations. Operating cost depends on token usage, which we minimise via prompt engineering, caching, model routing (cheap models for easy queries), and batch processing. We show transparent dashboards of cost per conversation and per task.
Can the AI agent work entirely on-premise?
Yes — we deploy open-source LLMs (Llama 3, Mistral, Qwen) on your infrastructure with GPU servers you control. RAG, vector stores, and orchestration all run inside your network. Suitable for regulated industries and data-residency-sensitive workloads.
How long does an AI agent take to build?
A pilot agent typically takes 4–6 weeks; a production-grade agent with full evals, observability, and integrations runs 10–16 weeks. We share a milestone plan with working demos at every sprint.
How do you measure if the agent is actually good?
We build an evaluation harness with ground-truth datasets, LLM-as-judge scoring, hallucination detection, refusal-rate tracking, latency and cost metrics. You get a numeric scorecard — not just a polished demo.