InnoBotAi — AI-Agent Development

AI-Agent Development

Design, Build & Deploy Production-Grade AI Agents

We craft autonomous and tool-using AI agents that plan, reason, and act — integrating with your apps, data, and workflows. From customer support and sales ops to DevOps copilots and back-office automations.

Tool-use (APIs, DB, RPA) Retrieval-Augmented Multi-Agent Orchestration Safety & Guardrails

50%+

Avg. task time saved

24×7

Always-on agents

99.9%

Uptime targets

Engagements include discovery → pilot → scale, with clear SLAs and safety reviews.

Core Capabilities

Autonomous Planning

Goal-driven task decomposition, scheduling, and self-reflection loops to reach outcomes reliably.

Tool & API Use

Secure connectors for databases, REST/GraphQL APIs, spreadsheets, email, calendars, RPA, and web.

Knowledge Retrieval (RAG)

Private docs become answers. Chunking, embeddings, and relevance feedback to reduce hallucinations.

Multi-Agent Systems

Specialized agents coordinate via protocols (planner/worker/reviewer) for complex workflows.

Safety & Guardrails

Policy checks, PII redaction, allow/deny tool lists, human-in-the-loop, and audit logs.

Observability

Tracing, cost & latency budgets, prompt/version control, evals, A/B tests, and rollback.

High-Impact Use Cases

Customer Support

Tier-1 deflection, ticket triage, disposition, and follow-ups across chat, email, and voice.

Sales Ops

Prospect research, CRM hygiene, meeting prep, and personalized outreach with approval gates.

IT & DevOps

Runbooks as code: incident summaries, log queries, rollout checks, and postmortem drafting.

Back-Office RPA

Invoice processing, reconciliations, data cleanup, and report generation across systems.

How It Works (Agent Loop)

1. Perception — ingest instructions, context, and constraints.

2. Reason — plan steps, choose tools, set success criteria.

3. Act — call APIs/DB/RPA, write, or orchestrate workers.

4. Reflect — evaluate results, retry or escalate.

5. Record — log traces, metrics, and artifacts.

Tech Stack & Integrations

Models & Routing

Open/closed LLMs, function calling, router policies by task, cost, and latency.

Retrieval

Embeddings, vector stores, chunking/metadata, freshness signals, evaluators.

Orchestration

Function hubs, workflows, queues, scheduled jobs, and human-in-the-loop steps.

Engagement Process

Discovery

Value mapping, data & tool audit, risks, and success metrics.

Pilot

Narrow scope, measurable KPI, and observability from day one.

Scale

Hardening, guardrails, cost control, rollout playbooks, training.

FAQ

How do you control hallucinations and risk?

Retrieval with citations, testable tool outputs, policy filters, eval suites, and human review for critical steps.

What about privacy and data security?

Least-privilege keys, PII redaction, audit logs, and optional on-prem/in-VPC deployment.

How fast can we launch a pilot?

Typical pilots go live in 2–4 weeks depending on scope and integration complexity.

Let’s Build Your First AI Agent

Tell us your top workflow, systems involved, and target KPI — we’ll propose a focused pilot plan.