Custom AI Development Services
CookMyTech is an AI development company in India engineering custom AI applications, autonomous AI agents, enterprise RAG systems, and scalable LLM software for startups, founders, and growing enterprises in India, the US, UK, and worldwide.
The Problem With Generic AI Wrappers
Off-the-shelf AI tools and simple wrapper scripts often fail in production. They suffer from high model latency, runaway API token bills, confidential data leaks, and unpredictable hallucinations that disrupt business workflows.
To build commercial AI products, companies need robust systems with caching layers, structured schema outputs, fallback models, and scalable vector databases.
Our AI Engineering Approach
CookMyTech designs production AI applications from the ground up. We build custom pipelines that bridge large language models with your business data, databases, and APIs.
- ✔ Deterministic JSON output parsing via Zod and Pydantic
- ✔ Vector search with sub-100ms response targets
- ✔ Enterprise security & private data guards with zero training retention
- ✔ Automatic API token cost optimization with semantic caching
Who Our AI Development Services Serve
AI SaaS Founders & Startups
Founders building AI-native products, vertical AI assistants, or intelligence features who need senior architecture without hiring expensive in-house AI research teams.
Enterprises Automating Workflows
Companies seeking custom internal software to process thousands of unstructured documents, automate customer support triage, or query complex private databases.
Product Leaders Integrating LLMs
Engineering teams embedding generative AI features, smart semantic search, or conversational copilots directly into existing software platforms.
What We Build in AI
Autonomous AI Agents
Multi-agent orchestrations with function calling, external tool usage, and automated multi-step decision workflows.
AI Agent Services →Enterprise RAG Systems
Document Q&A engines querying PDF, SQL, and internal knowledge bases using vector embeddings and semantic search.
RAG Services →AI SaaS Products
Full-stack multi-tenant AI applications complete with Stripe subscriptions, rate limits, and usage analytics.
AI SaaS Services →Our AI Development Process
Discovery & AI Architecture Blueprint
We analyze your business bottlenecks, evaluate model feasibility (RAG vs fine-tuning vs agent loops), and define structured input/output contracts.
Data Pipeline & Vector Indexing
We parse and clean your unstructured documents (PDFs, SQL databases, Notion, tickets) and index them into low-latency vector databases (Pinecone, Qdrant, pgvector).
Model Integration & Guardrail Engineering
We connect foundation models (GPT-4o, Claude 3.5, Llama 3) with deterministic schema validation, token spend caps, and Redis semantic caching.
Automated Evaluation & Red-Teaming
We benchmark system accuracy using RAGAS and synthetic test suites to verify sub-100ms vector search latencies and 0% critical hallucinations.
Production Deployment & Observability
We deploy containerized worker nodes to AWS or GCP with OpenTelemetry tracing, real-time token telemetry dashboards, and 100% IP transfer.
Project Deliverables
We ship clean, tested code with 100% intellectual property transfer and zero vendor lock-in.
- ✔Production-grade AI application repository with strict TypeScript or Python type safety
- ✔Deterministic schema validation layers ensuring 100% parseable structured JSON outputs
- ✔High-speed vector database pipeline with hybrid semantic and keyword BM25 search
- ✔Redis semantic caching layer reducing repeat LLM token expenses by up to 60%
- ✔Automated continuous evaluation benchmark suite tracking faithfulness and precision
- ✔Containerized Docker configurations and automated CI/CD deployment pipelines
- ✔100% intellectual property transfer, prompt manifests, and technical architecture manual
Security, Privacy & Cost Controls
Deploying AI software requires strict governance:
- • Token Spend Caps: Hard limits per organization and user to eliminate unexpected API charges.
- • Prompt Injection Defense: Sanitized input layers preventing malicious prompt overrides.
- • Zero Training Retention: Enterprise API routing ensuring private customer data is never used for model training.
- • Full Source Code Ownership: 100% intellectual property rights transferred upon final milestone.
AI Technology Stack
Related Engineering Services & Guides
Frequently Asked Questions
How much does custom AI software development cost in India?
AI software development costs in India vary based on architectural scope: integrating commercial LLM APIs with vector databases starts at a much lower cost than training custom open-source models or building multi-agent autonomous graphs. CookMyTech provides transparent, fixed-scope sprint pricing with zero hidden maintenance fees.
Can you integrate OpenAI, Claude, or open-source Llama models into our existing software?
Yes. We design enterprise AI integrations using OpenAI API, Anthropic Claude, Llama 3, and private VPC-hosted models with secure API gateways, rate limiting, and semantic token caching to protect operating margins.
How long does it take to build an AI application or MVP?
A functional AI MVP or prototype featuring LLM tool calling, vector search, or autonomous workflow automation typically ships in 3 to 6 weeks depending on data preparation and schema validation requirements.
Do clients own the intellectual property and code?
Yes, 100%. Clients own all Git repositories, custom prompt pipelines, evaluation test suites, and cloud infrastructure deployment scripts with zero vendor lock-in.
How do you prevent hallucinations in commercial AI systems?
We implement deterministic guardrails: structured JSON schema validation (Zod/Pydantic), hybrid Retrieval-Augmented Generation (RAG) with exact document citations, cross-encoder reranking, and verification reflection loops that validate answers before delivering them to users.
Ready to build custom AI software?
Talk directly with senior AI engineers to discuss your architecture, data, and timeline requirements.