Services · AI Systems Integration

Connect AI to the systems and data your business already runs.

We build the integrations, governed data access, operational software, and control surfaces an AI capability needs to work in production. Custom software is supporting infrastructure—not a disconnected development project.

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What's included

Capability-first. Designed for what comes next.

Every client has a different stack. We build on the capabilities that fit your operation: a modern web application layer, a managed data layer, a workflow-orchestration layer, the Claude API for intelligence, payment processing, and transactional communications. Every choice has a reason. Every reason is architectural. The architecture is consistent because consistency is what lets Phase 2 deploy onto what Phase 1 built.

Models we work with

Claude

Primary across most builds

GPT

Where it's the right fit

Gemini

Long-context + multimodal

Open-weight

On-prem & sovereign

All tool subscriptions on your card, direct to the vendor. Never marked up.

Custom Software & Web Applications

Ground-up custom builds for how your operation actually runs: web applications, internal platforms, customer-facing products. Built when off-the-shelf genuinely fails, on the same managed data layer everything else runs on, so nothing you commission becomes an island.

Product Configurators

A real-time pricing engine backed by a managed database, with a modern web front end. Every submission connects directly to the proposal automation pipeline: configure, submit, receive proposal, sign, pay. The configurator is not a form; it is the front end of the revenue system.

Client & Partner Portals

Per-client scoped live views pulled from your CRM, project management system, and deliverables platform. The client sees exactly what they need and nothing they don't. Built on a modern web stack with row-level security: no BI tool, no third-party portal platform, no recurring per-seat pricing.

Data Pipeline Architecture

Connect your POS, CRM, booking platform, and accounting system into one source of truth. The single source is what makes every downstream automation possible. Without it, you have four systems each 70% accurate. With it, you have one system that is correct.

Custom Dashboards + Control Planes

Built on a modern web stack, not in a BI tool. The distinction matters: a BI tool config is a visualization layer over messy data. A custom control plane is an operating surface with live data, actionable triggers, and logic you own. Not a chart. An instrument.

Payment Infrastructure

Card and ACH payments, commercial card programs. Recurring invoicing, deposit-on-signature, event-triggered payment links, and ACH routing to cut per-transaction costs. Payment infrastructure is not a bolt-on. It is designed at the same time as the data model.

AI Visibility Architecture

Site structure that search engines and AI answer engines (ChatGPT, AI Overviews, Perplexity) can read correctly, schema markup for every content type, Core Web Vitals tuned, Google Business Profile connected once. This is an architectural decision made when the URL structure and page hierarchy are first designed, not a service we run monthly. Retrofitting is always more expensive.

MCP Server Architecture / Headless Control Plane

Expose approved business capabilities as permissioned tools through the Model Context Protocol where it fits. This can reduce model and interface coupling, but changing a model still requires regression evaluation, security review, and a controlled rollout because tool use and output behavior differ.

E-commerce + Booking Integrations

Third-party booking platforms, point-of-sale systems, and property-management systems, pulled into the automation layer so every booking event, transaction, and status change triggers the downstream workflows. The platform you already run becomes a trigger, not an island.

Mobile-Ready / PWA Builds

Progressive Web App architecture for operations that need field access without a native app build. Offline-capable, installable, and fully integrated with the same managed data layer the rest of the stack runs on. One data model, two surfaces.

CMS Integration

Non-technical staff publish content without a developer in the loop. A headless CMS of your choice: the decision is driven by your team's workflow, not by what we prefer. The content layer is decoupled from the presentation layer so both can evolve independently.

Internal Tooling

Slack bots and command interfaces that let your team query live operational data in plain English. What did we book last week? What's outstanding on account X? Who hit their target? The answer lives in your data layer. The Slack interface makes it accessible without a login, a dashboard, or a SQL query.

Foundation Layer Builds

Website, DNS, Google Business Profile, analytics, and sitemap: the baseline infrastructure every business needs before anything else can run on top. Never sold as 'a website.' Sold as the foundation your automation stack runs on. The distinction changes how the architecture is designed.

API & Integration Layer

A durable integration surface that connects your core systems to each other and to external services, designed once, not re-plumbed every time a new tool enters the stack.

Search & Recommendations

Relevance-ranked search and personalized recommendation surfaces built on your own data. Not a third-party widget bolted to the front end.

Auth & Role-Based Access

Authentication and authorization designed at the data-model level: permissions enforced at the row, not just the page, so access control is structurally sound rather than patched on top.

Performance & Accessibility Hardening

Core Web Vitals tuned, WCAG compliance verified, and load performance profiled under real conditions. Not just benchmarked on a fast connection in a clean environment.

Technical Due Diligence

An independent review of a tech stack before a deal or a raise: architecture quality, data model, security exposure, vendor lock-in, integration debt, and what an acquirer will ask about first.

Privacy & Data Compliance Systems

Data-flow mapping, consent management, access-request and deletion pipelines, and the documented architecture your legal team needs for GDPR and CCPA readiness.

How it works

Map the data. Build on the standard. Design for tomorrow.

The three-stage sequence is how we avoid the most expensive mistake in custom software: building the right feature on the wrong foundation. Every engagement begins with the integration diagram, not the design mockup.

Step 1

Map the data

Before a line of code is written, we produce a full integration diagram: every data source, every relationship, every flow. The schema is designed at this stage, not discovered during development. The data model is the most expensive thing to get wrong and the cheapest thing to get right.

Step 2

Build on the right capabilities

A modern web stack for the front end and deployment. A managed database layer for data, auth, and storage. A workflow-orchestration layer. The Claude API for intelligence. Payment processing. The capability set is matched to your operation, consistent in architecture because consistency is what lets each phase build on the last.

Step 3

Design for tomorrow

The data model is built once, correctly, so Phase 2 automation plugs in without a rebuild. The MCP hooks are in place so Phase 3 AI doesn't require a new architecture. The payment layer is designed at schema-time, not bolted on. Every decision at Phase 1 is made with Phase 3 in mind.

In practice

A configurator that runs the revenue process end-to-end.

A custom cabin manufacturer needed a public-facing configurator for nine models with dozens of options each: size, layout, exterior, interior, add-ons. Every combination needed real-time pricing. Dealer pricing needed to be accessible but gated behind a password. Every submission needed to feed directly into the proposal pipeline.

Phase 1 is live on a modern application stack with a managed database layer: a real-time pricing engine, a dealer-pricing toggle controlled by row-level authorization, and the full product lineup. Phase 2 is designed to connect submissions to proposal generation, e-signature, and deposit collection without manual re-entry; that workflow is not yet live.

The architecture separates approved business capabilities from the model layer. That reduces model coupling, but a future model change would still require regression evaluation, prompt or adapter changes where needed, security review, and a controlled rollout. The business owns the architecture and can evaluate alternatives without rebuilding the entire operating system from scratch.

See the product configurator we built

Full lineup

Live in the configurator at launch, each fully priced in real time

Real, from the engagement

~$20/mo

Phase 1 platform infrastructure to run the live configurator

Real, verified at go-live

Designed

Phase 2 submit-to-signature-and-deposit workflow; not yet live

Controlled

Model changes require evaluation, security review, and monitored rollout

Models we work with

We don't sell you a model. We build the architecture that lets any model do the work, and we're fluent across the families that matter. We pick the right one for the job, and you can swap it later without a rebuild.

Claude

Anthropic

Primary across most builds

GPT

OpenAI

Where it's the right fit

Gemini

Google

Long-context + multimodal work

Open-weight

Llama · Mistral · Qwen

On-prem & sovereign deployments

Model-agnostic by architecture. The model is a config decision; the system around it is the asset.

Pricing logic

Priced on complexity. Never on hours.

Fixed for the build. Recurring for maintenance. Tool subscriptions are always client-paid, direct to the vendor. Never marked up.

Foundation Layer

Website, DNS, Google Business Profile, analytics, sitemap, Core Web Vitals baseline. The infrastructure every automation layer runs on. Never sold as 'a website': sold as the foundation.

Fixed for the build

Mid-complexity build

Configurator, client portal, or custom dashboard with 1 to 2 external integrations. Scoped explicitly before work begins. Price is fixed; no hourly billing.

Fixed for the build

Full platform build

Multi-system builds: MCP layer, data pipeline architecture, multi-integration platforms. Quoted after the integration diagram is complete, not before. Exact numbers are sized to your operation and put in writing before you commit.

Scoped in the diagnostic

MCP Architecture Layer

A headless control plane that exposes approved business capabilities to supported AI models as structured tools. Each model integration is evaluated, adapted, security-reviewed, and validated before controlled rollout. Included in full platform builds; can be added to existing systems.

Fixed for the build

Maintenance

Monitoring, dependency updates, failure detection, iteration. Required on every active system. Sized to what is under management.

Recurring for the run

Questions

Straight answers.

We already have a website, a CRM, and a booking platform. Are you replacing all of that?

No. We build the intelligence and automation layer on top of what you already run. The goal is not to replace your existing tools. It is to connect them into a system that actually works. Replacement only makes sense when the existing tool is the architectural problem, not just a preference.

How is this different from hiring a web developer?

A developer builds what you specify. We architect what you will need in 12 months: the data model designed for the automation layer that comes in Phase 2, the MCP hooks built in so Phase 3 AI doesn't require a rebuild, the payment infrastructure designed at schema-time. The deliverable is not a website. It is infrastructure.

Can we change AI models later?

Usually, if the architecture separates approved business tools and context from the model layer. A model change is still a controlled release: we re-run evaluations, review tool behavior and security boundaries, update prompts or adapters where needed, and roll out with monitoring rather than assuming identical behavior.

Continue

Strategy

The diagnostic that defines what to build and in what sequence.

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Operations

The automation layer that runs on the infrastructure we build.

Explore

AI Architecture

The intelligence layer that plugs into the MCP foundation.

Explore

Engagement starts here

Start with the diagnostic.

Thirty minutes. We map your operation, name what's actually slowing it down, and tell you what we'd do if we were running it. You get a written stack assessment after the call, whether you hire us or not.

Book a Diagnostic Take the AI Readiness Assessment

Not limited to what's listed. Every engagement starts by assessing what your business actually needs, and we build whatever it requires.