Custom AI Integrations for Real-World Businesses

What we ship

• RAG (Retrieval-Augmented Generation) systems — answer questions over your private documents, knowledge bases, or product catalog with grounded citations.
• Document processing pipelines — extract structured data from PDFs, invoices, contracts, scanned forms. Confidence scores, human-in-the-loop review, audit trails.
• LLM automation and agentic workflows — multi-step processes that previously needed an analyst, now running on a schedule with eval guardrails.
• Embeddings and semantic search — find what your users mean, not just what they typed. Hybrid lexical-plus-vector retrieval that beats keyword search on real corpora.
• Classification and routing — categorize support tickets, leads, documents, or transactions at scale, with feedback loops that improve over time.
• Production-grade evals — test suites that catch regressions before users do. Without evals, you don't have an AI system, you have a guess.

How we work

AI projects fail for the same reason most software projects fail: vague scope, no ground truth, and no plan for what happens after the demo. We do three things differently.

1. Scope to one workflow. "Add AI to our app" is not a project. "Extract line items from these 200 invoice PDFs and write them to NetSuite" is. We start with one workflow, ship it, and earn the right to expand.
2. Build the eval first. Before we touch a prompt, we capture 20–50 real examples with the correct answer. The eval is the contract — if it passes, the feature is done. If it fails, we know exactly what broke.
3. Plan the failure mode. Every AI integration we ship has a confidence threshold and a human fallback. The model handles the 80%; the team handles the 20%. That is what production AI looks like.

Stack

We pick stacks based on where your data already lives and what your team can maintain after we hand off. Vendor-neutral, with strong opinions about which trade-offs matter.

Where this fits

We pair AI integrations with our broader work in React and .NET application development and the ViewForge Shopify fitment platform. Most clients hire us to build the application first, then add AI to the workflows that need it. Some hire us purely for an integration layer over an existing system. Both work.

Real examples are on the work page, including a multi-tenant React dashboard and the ViewForge Smart Parse pipeline (which extracts structured fitment data from unstructured Shopify product copy).

FAQ

What counts as a "custom AI integration"?

Anything that takes a foundation model (Claude, GPT, open-source) and wires it into a real business workflow — your data, your tools, your users. That includes RAG over internal docs, intelligent document extraction, support automation, classification pipelines, and agent loops that act on your systems.

Do you build chatbots?

Sometimes — but a chatbot is rarely the right shape. Most AI value comes from invisible pipelines: a process step that used to need a human now happens automatically, with a confidence score and a fallback. We build that. If a chat surface is genuinely the right interface, we build that too.

Which models do you use?

Whichever fits the job. Claude (Anthropic) and GPT (OpenAI) for most generation and reasoning tasks. Open-source (Llama, Mistral) when data residency or cost requires it. Embedding models from OpenAI or Voyage for retrieval. We are model-agnostic and rebuild around new models when the trade-offs justify it.

How do you keep AI from hallucinating?

Three things, roughly: ground every answer in retrieved context (RAG), force structured output where the downstream system needs it, and surface a confidence score so a human reviews the low-confidence cases. "Hallucination-free" is marketing. "Caught before it ships" is engineering.

How long does a typical integration take?

A focused integration — say, an extraction pipeline for one document type, or a RAG endpoint over one knowledge base — is 2–6 weeks from kickoff to production. Larger systems with multiple agents, evals, and integrations run longer. We scope tightly and ship in increments.

How do you handle data privacy and compliance?

For sensitive workloads, we use providers with zero-retention API tiers (Anthropic and OpenAI both offer them), keep PII out of prompts when possible, and design audit trails into the pipeline. For regulated industries we can deploy entirely on customer infrastructure with self-hosted models.