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
AI Workflow Automation in Bangalore
LLM integrations, AI decision systems, intelligent data processing, and autonomous agents. We design AI workflows that work reliably at production scale, with observability, guardrails, and human oversight where the stakes demand it.
// Services
AI workflow services
- RAG for private business knowledge
- LLM-powered document processing
- AI quality classifiers and routers
- Custom agent tool development
- LLM observability and cost tracking
AI Process Integration
Embed AI capabilities into existing business workflows.
AI Decision Systems
Automated decisions with human-in-the-loop review patterns.
LLM & GPT Integrations
GPT-4, Claude, Gemini, API integrations for your product.
AI Data Processing Workflows
Extract, classify, and transform data with AI models.
Custom AI Agents
Autonomous agents that operate within defined task boundaries.
// Standards
AI engineering standards
Task-appropriate models
Using GPT-4o for simple classification wastes money and adds latency. We select the smallest, fastest model capable of the task, and benchmark before deciding.
Guardrails and validation
Output validation, JSON schema enforcement, toxicity filtering, and PII detection on every AI output that reaches users or downstream systems.
Retrieval-augmented generation
RAG reduces hallucination risk for knowledge-intensive tasks. We design chunking strategies, embedding models, and retrieval pipelines specific to your data.
LLM observability
Every LLM call traced with LangSmith or Langfuse, prompt versions, token costs, latency, and output quality tracked across model versions.
PII and data handling
AI workflows that process personal data include PII detection and masking before sending to third-party model APIs, with clear data processing agreements.
Human review loops
Low-confidence AI outputs route to human review queues. We never design fully autonomous AI systems for consequential decisions without human oversight mechanisms.
// Technology
AI workflow technology stack
LLM APIs
OpenAI GPT-4oClaude 3.5Gemini 1.5MistralLlama 3Cohere
Orchestration
LangChainLlamaIndexLangGraphCrewAIAutogenFlowise
Vector Stores
PineconeWeaviateChromaQdrantpgvectorRedis Vector
Backend
PythonFastAPINode.jsNext.js API RoutesCeleryBullMQ
Data Processing
PandasPolarsDuckDBApache SparkdbtAirbyte
Monitoring
LangSmithLangfuseHeliconeArize PhoenixDatadogPostHog
// Process
From use case to monitored AI workflow
01
Use Case Discovery
2–3 daysIdentify high-value AI integration points in your workflows. Map data availability, quality, volume, and latency requirements that will constrain model selection.
// 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.
Gnani.ai (voice AI)
- Challenge
- Product surfaces needed performance-stable integrations with analytics and API-heavy workflows.
- Simplileap solution
- Custom API layer, observability hooks, and performance budgets on critical user paths.
- Outcome
- Reliable production delivery with maintainable codebase for ongoing feature work.
// 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
Founded
November 2020
Office
Residency Rd, Bengaluru, India
// FAQ
Common questions about AI workflow integration
Which AI models do you work with?+
OpenAI GPT-4o and GPT-4o-mini, Anthropic Claude 3.5 Sonnet and Haiku, Google Gemini 1.5, Mistral, and open-source models via HuggingFace or Ollama for on-premise deployments. Model selection is based on the specific task requirements.
Can you build AI features that work on our private data?+
Yes, through RAG (Retrieval-Augmented Generation) we index your private documents, databases, or knowledge bases into vector stores, enabling the LLM to answer questions about your specific data without fine-tuning.
What are AI agents and when do they make sense?+
AI agents are LLMs with tool use, they can call APIs, query databases, send messages, and make sequences of decisions autonomously within defined boundaries. They make sense for multi-step tasks where the workflow is complex and variable.
How do you prevent AI hallucinations in production?+
Structured output schemas (JSON mode), retrieval augmentation for factual tasks, confidence thresholds that trigger human review, citation requirements for knowledge-based responses, and comprehensive output validation before downstream use.
How do you handle data privacy when using OpenAI or Anthropic APIs?+
We can configure API settings to opt out of model training on your data. For sensitive use cases, we implement PII masking before sending data to third-party APIs, or recommend on-premise open-source alternatives like Llama 3.