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AI built for the work your team actually does.

Off-the-shelf AI tools do not fit real operations. Your team needs AI that lives inside your real workflows, grounded in your real data, integrated with the systems they already use. We design, build, deploy, and operate custom AI platforms end-to-end — not a prototype, not a demo, not a configured product handed off.

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The problem

Generic AI tools do not fit real operations. Consumer-grade AI assistants handle generic tasks. Real businesses run on specific data, specific workflows, and specific decisions — none of which a generic AI is built around.

Bolt-on AI sits outside the work. When AI is a separate tool people have to switch into, adoption falls and value never compounds. The AI that earns its keep is the AI inside the system the team is already using.

Most AI projects ship a demo, not a system. The deployment that worked on stage breaks in production because nobody architected for real workloads — capacity, latency, monitoring, model updates, security. The team that built it has moved on by the time the system needs operations.

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What we do

Custom AI applications engineered for your business — designed against your real workflows, built on your real data, deployed where your stack lives, and operated by the team that built them.

Customer-facing AI

Conversational discovery, intelligent search, recommendation, and concierge agents embedded directly into your storefront, portal, or app. Reference build: a 19,000-piece animation art gallery brought online with a private-AI catalog assistant — first-year online sales contributed a 30% revenue lift.

Internal copilots and agents

AI that lives inside your team's daily tools — sales, service, operations, finance, HR — grounded in your real data, integrated with your systems of record, with audit trails preserved.

Document intelligence

Production-grade pipelines for document intake, classification, extraction, summarization, and routing. Contracts, claims, clinical notes, application packages, RFP responses — at scale, not as demo-ware.

Private retrieval and RAG

Retrieval-augmented systems grounded in your proprietary data — policy libraries, product catalogs, internal knowledge bases, case files, historical records. AI that answers from your truth, not from the public web.

Decision systems

AI that informs or automates judgment calls in your operation — credit decisions, claims triage, lead scoring, fraud detection, capacity planning. Designed with the human-in-the-loop boundaries your business actually needs.

Vision and multimodal pipelines

Image and video analysis for classification, inspection, cataloging, quality control, and discovery. Deployed at production scale.

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Business outcomes

Production AI in 4 to 12 weeks

Most first-capability builds ship to real users in under three months. Not a pilot. Not a slide deck. Working software your team uses on day one.

Working software, not demoware

Every system we ship is architected for production from day one — capacity, latency, monitoring, security, model updates, rollback. The team that built it operates it after launch.

Right model for each workload

Open-weight models (Llama, Mistral, Qwen, DeepSeek) on private infrastructure where it earns out. Frontier APIs (OpenAI, Anthropic) where capability matters and procurement allows. Traditional code wherever AI is not the right answer. Documented per workload.

Measurable business impact

Every engagement is scoped against business outcomes — hours reclaimed, revenue lift, error rate reduction, decision speed. We instrument the system so the impact conversation is not theoretical.

No vendor lock-in

Open-weight models, documented architectures, portable deployments. If you ever decide to bring the system in-house, you own the architecture and the operational runbook.

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How it is priced

Engagement model
AI Platform Development

Fixed-price for the first capability shipped to production, scoped after the AI & Modernization Assessment. Typical first-capability builds run $50,000 to $250,000 depending on integration complexity, data work, and capability scope.

Hosting in the secure deployment environment of your choice is included by default. Most clients transition into a monthly Managed AI Operations engagement after launch — those terms are negotiated alongside the build scope so total cost of ownership is visible up front.

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AI Platform Development — frequently asked questions

The questions we get most often, answered. If yours isn't here, ask it on a 30-minute call — we answer the awkward ones too.

What kinds of custom AI applications do you build?
Conversational and agentic systems, document intelligence (intake, classification, extraction, routing), private retrieval-augmented systems (RAG over proprietary data), vision and multimodal pipelines, and integration-heavy systems-of-record applications. Real production systems, not chatbot demos. See representative work.
How long does a custom AI application build take?
4–12 weeks end-to-end for most engagements: 1–2 weeks Discovery + scoping → 1–2 weeks design + architecture → 2–8 weeks build → 1–2 weeks integration + testing → 1 week deployment + training → ongoing operations.
How is pricing structured?
Fixed-price project engagements. Scope is defined in the Discovery phase. The price is fixed for that scope. Changes in scope trigger a change order — no surprise invoices, no time-and-materials drift. Hosting in our private AI cloud is included by default; Managed Operations after launch is a separate monthly engagement, typically bundled.
Do you integrate with our existing systems?
Yes — and we treat integration as a first-class problem, not a last-week scramble. Most of the interesting AI work in a real enterprise lives at the seams (CRM, ERP, EHR, POS, payment, case management). We've integrated with Epic, Salesforce, Magento, custom ERPs, twenty-year-old on-prem systems, and modern SaaS APIs. We are honest in scoping about which integrations are easy and which are hard.
Where will my data be hosted?
By default, in our private AI cloud — Tier III TierPoint colocation facilities in Marlborough, MA (MRL-01) and Chicago, IL (CHI-01), with regional capacity at our Connecticut office (CT-01). For workloads with strict data sovereignty, ITAR, or air-gapped requirements, we install the entire Skyview stack on-premises in your own data center. Every engagement includes a written data flow document covering every component, every integration, and every external API call.
Do you operate the system after launch?
Yes. The team that builds your system is the team that runs it. Most engagements transition into a monthly Managed AI Operations agreement covering hosting, monitoring, model and dependency updates, performance tuning, security patching, and ongoing refinement against real usage. The engineers who designed the system stay accountable for it — six months and three years from now.
Can you deploy on-premises at our data center?
Yes. We design, build, and install the full Skyview stack — self-hosted models, vector databases, retrieval pipelines, observability — inside your perimeter. Air-gapped supported. We spec the hardware, procure it, rack it, and operate it remotely (with documented access) or on-site as your security posture requires. This is the right answer for federal-adjacent workloads, ITAR-covered work, regulated healthcare, and enterprises with data residency mandates that exceed our cloud's posture.
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Scope an AI platform build

Tell us what you want to build. We will tell you what it would actually take to engineer, ship, and run.

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