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
LLM & GPT Integrations
LLMs are transformative but unpredictable. We build production-grade LLM integrations with structured outputs, RAG pipelines, fallback handling, and cost management, so AI delivers value reliably, not just impressively in demos.
// Key benefits
What makes this service valuable
Production-grade reliability
LLMs have variable latency, occasional failures, and schema drift. We build integrations with retry logic, output validation, structured JSON extraction, and fallback strategies.
RAG pipeline architecture
Retrieval-Augmented Generation grounds LLM responses in your specific documents and data, dramatically improving accuracy and reducing hallucination for knowledge-base applications.
Cost and latency optimisation
LLM API costs scale with token usage. We optimise prompts, implement caching, route to appropriate model tiers, and monitor cost per operation.
// Details
LLMs in production, not just prototypes
Most LLM integrations work in demos and fail in production. The difference is in engineering: structured output parsing, retry handling, prompt version management, output evaluation, and cost monitoring.
We use LangChain or LlamaIndex for complex LLM orchestration, direct API integration for simpler use cases, and Instructor or Pydantic for structured output extraction.
// What this includes
- OpenAI GPT-4 / o1, Anthropic Claude, Mistral
- Structured output extraction (JSON mode / Instructor)
- RAG pipeline with vector database (Pinecone, Weaviate, pgvector)
- Prompt template management and versioning
- LLM output evaluation and quality scoring
- Streaming response handling
- Cost monitoring and optimisation
// Deliverables
What you receive
Every engagement produces clear, documented deliverables. Here is exactly what is included in our llm & gpt integrations service.
- 01LLM integration with chosen provider
- 02RAG pipeline with vector database (if required)
- 03Structured output extraction
- 04Prompt template library
- 05Cost and quality monitoring
- 06Integration documentation and evaluation framework
Production patterns and experience depth: Python expertise ›
// In practice
How llm & gpt integrations engagements run
We typically anchor the first sprint on production-grade reliability. LLMs are transformative but unpredictable. We build production-grade LLM integrations with structured outputs, RAG pipelines, fallback handling, and cost management, so AI delivers value reliably, not just impressively in demos. On Residency Road engagements, discovery maps dependencies and success metrics before sprint one. Every automation ships with exception queues, audit logs, and a baseline metric so ROI is measurable within 30 days.
// 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
// 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 llm & gpt integrations
OpenAI vs Anthropic vs open-source, which should I use?+
GPT-4o is the most capable for general tasks with the best ecosystem. Claude excels at long context and nuanced instructions. Open-source (Llama, Mistral) is cost-effective for high-volume, privacy-sensitive, or fine-tuning use cases. We recommend based on your specific requirements.
What is RAG and when do I need it?+
Retrieval-Augmented Generation retrieves relevant documents from your knowledge base and includes them in the LLM context, allowing the model to answer questions about your specific data without fine-tuning. Use it when you need the LLM to know about your products, policies, or documents.
Ready to get started with llm & gpt integrations?
Share your requirements with our team. We respond within one business day with a clear plan from discovery to delivery.