Generative AI development services grounded in your data.

Our generative AI development services start with one question: which task gets measurably better with a language model, and how will we know? We run use-case discovery with your team, benchmark prompts and models on real examples, then build the retrieval pipeline, guardrails and interface around what wins. The result is a production system with monitoring and cost controls, not a prototype that only works in a demo.

As a generative AI development company that also builds web and mobile products, we ship the whole feature: the chat or copilot UI, the backend, the integrations and the cloud deployment. You don't need to coordinate an AI vendor, a product agency and an infrastructure team, and nothing gets lost between them.

• Use-case discovery and agreed success metrics
• Retrieval pipelines (RAG) over documents, wikis and databases
• Prompt and model benchmarking on your real examples
• Guardrails, red-teaming and evaluation suites
• Chat and copilot UI for web and mobile

Retrieval-augmented generation lets a model answer from your content instead of its training data. Our RAG development services cover the parts that decide quality: cleaning and chunking documents, choosing embeddings, hybrid keyword and vector search, re-ranking, permission-aware retrieval so people only see what they're allowed to, and citations on every answer. We build on pgvector, Qdrant, Pinecone or Elasticsearch, depending on scale and where your data must live.

Typical uses include policy and SOP assistants for operations teams, support answer drafting from a help center and ticket history, proposal and report drafting over firm documents, and internal search across CRM notes, wikis and shared drives. Each one gets a test set of real questions with known answers, so retrieval quality is measured rather than assumed.

A copilot works beside a person inside the tool they already use: drafting, summarizing, suggesting the next step and filling in forms. Our AI copilot development services cover in-product copilots for SaaS platforms and internal copilots for sales, support and finance teams. The design matters as much as the model: when the copilot appears, how it shows where an answer came from, and how a user accepts, edits or rejects a suggestion.

We have shipped this pattern in our own products. iSales includes a role-aware AI copilot for admissions teams, and the M361 AI Writing Studio turns a brief into a draft blog post with brand voice, a live brief-quality score and transparent per-article AI cost. Building both taught us to put cost and quality in front of users instead of hiding them.

As an LLM development company, we aren't tied to one provider. We benchmark OpenAI, Claude, Gemini and open-weight models such as Llama, Mistral and Qwen on your task, then choose on accuracy, latency, cost and data rules. Where data can't leave your environment, we deploy open-weight models in your private cloud or on-premises with vLLM or Ollama.

Custom LLM development rarely means training a model from scratch. More often it means better retrieval and structured prompts, and occasionally fine-tuning a smaller model on your examples when it beats a larger general model on cost or consistency. We control running costs with model routing, semantic caching and per-feature budgets, so unit economics hold up as usage grows.

• Model routing that sends simple requests to smaller, cheaper models
• Semantic caching for repeated questions
• Per-feature budgets and cost dashboards
• Private-cloud or on-prem deployment for sensitive data

Language models make things up when they lack the right context. We reduce that with grounded retrieval, answers that must cite sources, a clear refusal when the evidence is missing, and structured outputs validated before anything reaches a user or another system. Guardrails screen inputs and outputs for prompt injection, data leakage and off-topic requests, following OWASP guidance for LLM applications.

Every system ships with an evaluation suite: real questions, expected answers and scoring for accuracy, faithfulness to sources and safety. It runs before each release and whenever a prompt, model or data source changes. Tracing and logs show every retrieval and model call in production, so when an answer is wrong you can see why and fix the cause.

Building a generative AI feature is the easy half. Keeping it accurate as your documents change, costs predictable as usage grows and quality steady across model upgrades is the hard half. We build and run our own products in production, and the same senior team that scopes your system builds and operates it. Code, prompts and evals live in your repositories, the system runs in your cloud, and 100% of the IP is yours.