Corporate law boutique
- Challenge
- 200-page dataroom first-pass review took 11 hours per matter.
- Simplileap solution
- Private RAG with citation anchors and human sign-off gates.
- Outcome
- Review time 11h → 3.5h; zero unverified citations in pilot.
// Automate
Custom AI Agents
AI agents go beyond single-shot LLM calls, they plan, use tools, and execute multi-step tasks autonomously. We build production AI agents with the reliability, observability, and human oversight that enterprise deployment requires.
// Key benefits
What makes this service valuable
Tool-use and function calling
Agents that browse the web, execute code, query databases, call APIs, and interact with external systems, using OpenAI function calling or LangChain tool definitions.
Multi-agent orchestration
Complex tasks require multiple specialised agents, researcher, writer, validator, executor, orchestrated by a coordinator agent. We design multi-agent systems with clear role separation and error recovery.
Human-in-the-loop checkpoints
Fully autonomous agents are risky in production. We design agents with configurable checkpoints where humans review and approve before high-risk actions are taken.
// Details
Autonomous AI that earns trust
AI agents work best for tasks that are: well-defined in outcome but variable in path, require multiple tool interactions, and have sufficient volume to justify the engineering investment.
We build agents using LangChain, LlamaIndex, or direct OpenAI Assistants API depending on complexity and tool requirements. Every agent has comprehensive tracing (LangSmith or custom) and human review interfaces.
// What this includes
- ReAct and Plan-and-Execute architectures
- Tool integration (web search, code execution, APIs)
- Multi-agent orchestration (CrewAI or custom)
- Memory systems (short and long-term)
- LangSmith / custom tracing
- Human approval workflows
- Agent performance evaluation
// Deliverables
What you receive
Every engagement produces clear, documented deliverables. Here is exactly what is included in our custom ai agents service.
- 01Custom AI agent implementation
- 02Tool definitions and integrations
- 03Human oversight interface
- 04Agent tracing and observability
- 05Performance evaluation framework
- 06Documentation and deployment guide
// In practice
How custom ai agents engagements run
Agents use function calling against a fixed tool allow-list — query CRM, create Jira tickets, run SQL against read replicas — with max step counts and cost caps per session. OpenTelemetry spans capture each tool invocation; humans approve actions above defined risk thresholds. Evaluation sets from real support transcripts run nightly in CI before prompt or tool schema changes ship.
// Stack & frameworks
Stack we use for this
AI & LLM
- OpenAI / Anthropic APIs
- LangChain pipelines
- RAG architectures
- Confidence thresholds
Integration
- Salesforce / HubSpot
- Zapier / Make
- Custom webhooks
- ERP connectors
Governance
- LangSmith observability
- PII handling
- Audit logs
- Rollback procedures
// Delivery
Simplileap execution framework
01
Architecture mapping
Dependencies, API contracts, compliance constraints, and performance budgets documented before sprint one.
02
Secure sprints
Two-week increments with GitHub access, demo recordings, and QA checkpoints, client visibility at every stage.
03
QA & handover
Automated tests on critical paths, security review, runbooks, and knowledge transfer to your team.
// Proof
Real deployments from Bangalore
B2B infrastructure software vendor
- Challenge
- Editorial team needed governed AI without off-brand drafts.
- Simplileap solution
- Constrained prompts, block patterns, and review-before-publish workflow.
- Outcome
- Draft cycle 4.2 days → 2.1 days.
// Engagement models
How teams engage us
Currency
| Package | Ideal for | Investment | Includes |
|---|---|---|---|
| Workflow automation | Ops teams | ₹3L – ₹10L |
|
| AI / LLM integration | Product teams | ₹4L – ₹12L |
|
| RPA implementation | Back-office | Scoped per process |
|
// Company and service positioning
Company and Service positioning is reviewed for production delivery standards by Harsha Parthasarathy (Co-Founder, Strategy & Operations 24+ years IT veteran, IBM, Global Delivery, Program Management) and Keshav Sharma (Co-Founder, Engineering and Lead Architect, Full-stack engineering, product delivery and technical standards).
CIN
AAU-8582
Startup India
DIPP83124
Founded
November 2020
Office
Residency Rd, Bengaluru, India
// FAQ
Common questions about custom ai agents
What tasks are AI agents best suited for?+
Research and information synthesis, multi-step process automation, customer service escalation, code review and generation, and data analysis workflows. Best results come from well-scoped tasks with clear success criteria.
How do you prevent AI agents from taking harmful actions?+
We design agents with principle of least privilege (tools limited to what the task requires), human approval gates for irreversible actions (sending emails, making payments), and action logging for audit.
Ready to get started with custom ai agents?
Share your requirements with our team. We respond within one business day with a clear plan from discovery to delivery.