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AI Engineering

Custom AI, LLM, and RAG Development for Production

When a packaged product does not fit, we build the AI system: LLM applications, retrieval pipelines over your data, fine-tuned and self-hosted models, evaluation harnesses, and the integrations that make it useful. Engineered for production from the first sprint, with guardrails, monitoring, and clear ownership.

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Who This Is For

Built for teams that need AI their vendors do not offer

  • Companies with proprietary data and workflows that off-the-shelf AI tools cannot reach.
  • Product teams adding AI features to their software and needing reliability, not demos.
  • Regulated businesses that must run models privately with full control of data and prompts.
  • Teams whose first AI prototype worked in a notebook and failed in production.

What We Build

AI systems, not experiments

LLM applications

Assistants, copilots, summarizers, classifiers, and generators built into your products and internal tools with clear interfaces and controls.

Retrieval-augmented generation

Ingestion, chunking, embeddings, hybrid search, re-ranking, and citation over your documents and databases, tuned on your questions.

Fine-tuning and self-hosting

Open-source models such as Qwen fine-tuned for your domain and served on your infrastructure with vLLM-class serving and monitoring.

Evaluation and safety

Test sets from real cases, automated scoring, regression checks on every change, guardrails for sensitive outputs and actions.

Integration and orchestration

LangChain and LangGraph workflows, tool use with permission scopes, PostgreSQL state, and APIs into your systems.

Voice and multimodal

Speech, vision, and document understanding combined with language models, including real-time voice on our Asterisk stack.

From notebook to production

Many AI prototypes work in a demo and fail in production for the same reasons: no evaluation, no grounding, no handling of bad inputs, no monitoring, and integrations left for later. DPI builds custom AI the other way around: the evaluation harness and the integration plan come first, and the model choice follows the constraints.

Choosing the right architecture

Hosted frontier models when capability and speed matter most; self-hosted open-source models when data residency, cost per request, or latency require it; retrieval to ground answers in current data; fine-tuning for domain language and smaller models. We make these choices with your engineers, against explicit latency and cost budgets, and document them so the system can be maintained.

Engineering for reliability

Every system ships with a test set from real cases, automated scoring, regression checks on every change, guardrails for sensitive outputs and actions, logging, and dashboards. Retrieval is tuned on your questions; tools carry permission scopes; state lives in PostgreSQL rather than in prompts. Your team gets runbooks and training.

What you measure

Accuracy on the evaluation set, latency and cost per request, task completion, escalation rate, and the business metric the system was built to move, reported monthly.

How It Works

How we deliver custom AI

  1. Problem and data discovery

    The task, the data, the users, the constraints, and the metric that defines success, documented before any model is chosen.

  2. Architecture and model selection

    Hosted versus self-hosted, retrieval versus fine-tuning, latency and cost budgets, hosting and compliance, reviewed with your engineers.

  3. Build with evaluation

    The evaluation harness is built first; every iteration is scored against it until quality and cost targets are met.

  4. Production and handover

    Deployed on your infrastructure or ours with monitoring, logging, and runbooks; your team trained to operate or extend it.

Stack & Integrations

AI engineering stack

OpenAI, Anthropic Claude, AWS Bedrock Self-hosted Qwen and other open-source models LangChain, LangGraph PostgreSQL, pgvector, Qdrant, Redis Python, TypeScript, Node.js Docker, Kubernetes, AWS, Cloudflare Open-source speech models and Asterisk voice stack GitLab and GitHub CI pipelines

Industries

Custom AI by industry

Manufacturing

Quality, documentation, and knowledge on the floor.

Engagement Models

Engagement models

Technical discovery

Two to three weeks: problem framing, data assessment, architecture, evaluation plan, and a build roadmap with cost and latency budgets.

Build

Fixed-scope delivery in increments, each scored against the evaluation harness, with production deployment and handover.

Managed AI

Ongoing evaluation, monitoring, model updates, and feature expansion, or support for your team running it.

FAQ

Questions about custom AI development

Do you build custom enterprise AI software, not just integrations?

Yes. When the workflow has no product behind it, we build the application: the data model, the services, the interface your team works in, and the AI layer inside it, on Node.js, Python, React, and PostgreSQL. It runs on infrastructure you control, the code is yours, and it is documented so another team can pick it up.

What do custom RAG development services include?

Source selection and ingestion, chunking and embedding strategy chosen against your content, a vector store such as pgvector or Qdrant, retrieval and reranking tuned on your real questions, citation handling, an evaluation set that scores answers before each release, and the refresh pipeline that keeps the index current. The retrieval design is where accuracy is won or lost, not the model.

What does a custom AI assistant cost to develop?

The build is a fixed scope set by the number of sources, systems it acts in, and the accuracy bar; a first production assistant is typically a low-to-mid five-figure project, with a monthly operating cost for hosting, evaluation, and tuning. Discovery is quoted separately and ends with the estimate in writing.

Should we fine-tune or use retrieval?

Usually retrieval first: it grounds answers in current data and is cheaper to maintain. Fine-tuning helps for style, format, and domain language, and for smaller self-hosted models. Discovery answers this with your data.

Can we run everything on our own infrastructure?

Yes. Open-source models can be served on your servers or cloud with the full pipeline, so data never leaves your environment.

How do you prevent hallucinations?

Grounding, constrained outputs, evaluation on real cases, and guardrails for sensitive outputs. We measure and report accuracy rather than promising a number.

Who owns the code and models?

You do. Systems are built on maintainable stacks with documentation and runbooks, and can be operated by your team or by DPI.

How long does a build take?

A focused LLM application or RAG system typically reaches production within weeks after discovery; larger systems are delivered in increments.

Related

Next Step

Bring one workflow. Leave with a production plan.

Tell us where calls, tickets, documents, or approvals pile up. We map the workflow, size the impact, and propose a deployment you can measure.

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