Generative AI Development Company for Enterprise Use Cases

A fixed-scope GenAI MVP runs from $20,000 and most production GenAI platforms fall between $60,000 and $200,000 depending on data volume, retrieval complexity, and whether fine-tuning is in scope. Enterprise GenAI infrastructure with private inference, multi-tenant guardrails, and SOC 2 ready audit trails starts near $250,000. Or hire a dedicated senior GenAI engineer from $1,850 per month, first week on us.

RAG first in most cases. It is cheaper, faster to ship, easier to update, and gives you citations. Fine-tuning earns its keep when you need a specific tone of voice, a narrow domain language, low-latency on-device inference, or you have plateaued on retrieval quality. We benchmark both against the same golden set on day one and pick by score, not by hype.

Mutual NDA before any data leaves your environment. Customer data stays in your cloud account on AWS Bedrock, Azure OpenAI, or self-hosted vLLM where you control the keys. We never use your data to train shared models. All fine-tuned weights, prompts, retrievers, and evaluation sets are your IP and transfer to your repository at every milestone.

OpenAI GPT-4o leads on multimodal and tool use latency. Claude 3.5 Sonnet leads on long reasoning, structured output, and instruction following. Llama 3.1 70B and Mistral Large win when you need self-hosted, cost-controlled, and fine-tunable inference. We benchmark the shortlist on your golden set during sprint 1 and recommend by accuracy, cost, and latency, not by brand.

Prompt compression with DSPy, streaming responses, semantic caching with Helicone, model fallback from a large model to a small one on easy queries, and batching for offline workloads. For self-hosted inference we use vLLM and TensorRT for throughput. Most production deployments cut per-request cost by 40 to 70 percent versus a naive GPT-4o baseline while holding quality.

A golden test set built with your subject matter experts before any prompt is written. Automatic scoring on Ragas for RAG, DeepEval for general LLM tasks, and TruLens for groundedness and faithfulness. Human review on a sampled slice every sprint. Eval is a deliverable on every PR. Nothing ships without a green eval run.

Grounded RAG with citation chunks returned in every response, strict JSON schemas with retry on invalid output, guardrails on input and output for PII and policy violations, refusal on low confidence retrieval, and a faithfulness score on every generation logged to LangSmith or Langfuse. Hallucination rate is a tracked SLO, not a soft target.

Yes. Optional retainer after launch. Drift monitoring on eval scores, prompt versioning, fine-tune refreshes on new labeled data, and migration to newer base models when they ship. Quarterly cost and quality review with a written report. 95 percent of clients extend past 12 months.

Yes. Daily standup in your time zone with overlap for US Eastern, US Pacific, UK, EU, Middle East, and Australian working hours. Dedicated engineers shift their hours to match yours on long engagements.

Yes. Mutual NDA before any technical discussion. We can use our template or sign yours. Typically turned around within 24 hours. You can talk to a senior GenAI engineer the same day.

Senior engineers only, no bait and switch. 350+ production builds in 35+ countries since 2015. NDA in 24 hours. Code, prompts, weights, and eval sets in your repository from day one. 7-day no-risk trial on dedicated engagements. ISO 9001 process discipline. Direct senior engineer access, no project manager filter. Transparent USD pricing.