Best AI Development Company for Custom AI Solutions and Agents

An AI development services company designs and ships AI products end to end. That includes generative AI features, AI agents, RAG systems, computer vision, NLP, predictive ML, and MLOps infrastructure. The engagement covers discovery, data preparation, model selection or fine tuning, evaluation harnesses, production deployment, and ongoing monitoring with cost guardrails.

A discovery engagement starts at 5,000 USD. An AI MVP sprint runs 20,000 to 60,000 USD. A dedicated AI pod costs 15,000 USD per month. A full production AI build typically lands 80,000 to 350,000 USD depending on data complexity, fine tuning needs, and agent depth. We share a detailed estimate after discovery.

A discovery engagement runs 2 to 4 weeks. An AI MVP with real users typically deploys in 6 to 10 weeks. A full production AI product runs 3 to 6 months. Multi agent systems and fine tuned models add another 2 to 4 months.

OpenAI (GPT-4, GPT-4 Turbo), Anthropic (Claude), Google (Gemini), Meta (Llama 3), Mistral, Microsoft (Phi). We host open weight models on AWS Bedrock, SageMaker, vLLM, or Triton. We help you select based on accuracy, latency, cost, context length, and licensing needs.

Yes. We design embedding pipelines with chunking strategy tuned to your content, store vectors in Pinecone, Weaviate, Qdrant, Chroma, or pgvector, run hybrid retrieval with reranking, and ship citation tracking so every answer points to the source. Evals run continuously to catch retrieval regressions.

Yes. Single agent and multi agent systems with tool calling, long term memory, planning, and safety rails. We use LangGraph, AutoGen, CrewAI, or custom orchestrators depending on the workflow. Every agent has eval coverage and audit logs.

Yes. LoRA, QLoRA, and full fine tuning on Llama, Mistral, and Phi. We optimize for cost and latency at serving time. Fine tuning is recommended only after RAG has been tested first, since fine tuning is more expensive to maintain.

Multi layer defense. Grounded prompts with retrieval. Output validation against schemas. PII redaction in inputs and outputs. Jailbreak detection. LLM as judge eval pipelines. Human in the loop review for low confidence cases. Citation tracking so users can verify claims.

Token budgets per request and per user. Model routing (cheap model first, escalate to GPT-4 only when needed). Prompt caching for repeated queries. Embedding caching. Rate limiting. Daily cost dashboards with alerting when budgets exceed thresholds.

Yes. Custom eval harnesses, golden dataset curation, LLM as judge scoring, human in the loop validation, and regression baselines. Every prompt and model change runs against the eval suite before promoting to production. Drift detection runs daily in production.

You own 100 percent of all training code, fine tuned model weights, prompt registries, eval suites, datasets, and infrastructure. Open weight models are licensed under their respective licenses (Llama, Mistral, Phi). API based models (OpenAI, Anthropic, Google) are governed by their API terms.

Yes. We work with clients across the US, UAE, Saudi Arabia, UK, Europe, and APAC. Delivery from India with business hours overlap aligned to your team. AI deployments we have shipped run in 35+ countries.