How much do custom AI solutions cost?

Most production custom AI projects run $75,000 to $250,000 for the initial system, plus an optional monthly operate-and-improve retainer. An assessment plus prototype alone runs $25,000 to $50,000, fixed fee. Our AI consulting cost guide publishes the fuller breakdown.

How long does custom AI development take?

Eight to sixteen weeks from kickoff to a running production system for a typical scope. Complex builds across multiple core systems or heavy compliance review push that to twenty to twenty-four weeks. Prototypes alone run three to six weeks.

Do we need a data science team to work with your custom AI development services?

No. Roughly half our clients have zero AI headcount when we start. We work alongside your existing IT, operations, and compliance teams, do the model and integration work ourselves, and transfer ownership at the end so a business analyst or a mid-level developer can operate the system day to day.

What is the difference between custom AI solutions and a Copilot or ChatGPT license?

Licensed assistants can support individual work and connect to approved information sources. A custom AI solution is built around a specific workflow, with the integrations, business rules, review steps, and monitoring that workflow requires. Test the capabilities of your existing licenses first; a custom build is justified only when those tools cannot meet the requirements.

How does a custom AI development company handle compliance (NCUA, HIPAA, FERPA, SOC 2)?

Compliance is a phase 1 requirement, not a phase 4 patch. We work inside your existing security review process, provide a data flow diagram and model card during design, and can deploy inside your VPC or on-premise when regulators require it. For NCUA-supervised institutions we align documentation to the current AI risk management guidance and the third-party risk framework your credit union already runs.

Who owns the model and the code at the end of the project?

You do. Every deliverable, source, model weight (where we train the model), and piece of documentation is yours. We work under a standard master services agreement with an IP assignment clause. No lock-in.

Custom AI Solutions

Custom AI Solutions and Development Services

We design and build custom AI solutions on your data, integrated with the systems you already run, deployed to production in 8 to 16 weeks. Consulting-led custom AI development for credit unions, healthcare organizations, AEC firms, and universities.

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What are custom AI solutions?

Custom AI solutions connect a defined business workflow to the data, rules, and systems it needs. A licensed assistant may already support approved apps and internal information, so missing data access alone does not justify a custom build. Start by testing the tools you own. Build only where the workflow needs integrations, approval steps, evaluations, or operational controls those tools cannot provide.

For a credit union working with a legacy core, test whether the proposed solution can access the required records, respect member-data permissions, and produce an audit trail. Those requirements may call for custom integration, but the answer depends on the existing platform and the workflow. Keep a named reviewer for consequential decisions.

When does custom AI development make sense?

Three conditions usually justify the investment:

The workflow you want to improve runs at least a few hundred times per month, so automation compounds.
Off-the-shelf tools cannot see the data required to answer the question well: regulated data, on-premise systems, or internal knowledge bases.
A wrong answer has a real cost (a denied loan, a missed diagnosis, a change order dispute) that justifies engineering guardrails around it.

If two of the three apply, custom AI development usually pays back inside a year. If only one applies, we recommend a subscription and no project.

What we build

AI copilots for internal teams. Retrieval systems grounded in your policies, procedures, and historical work product, so member service reps, loan officers, project managers, or advisors get accurate answers pulled from your documents instead of guesses. Every answer cites the source document by name.

Machine learning models on your data. Fraud scoring, delinquency prediction, patient no-show forecasting, bid-margin models, enrollment yield models. Trained on your history, monitored in production, retrained on a schedule.

Generative AI and custom LLM builds. Private, policy-grounded assistants that draft correspondence, summarize records, and answer employee questions using your knowledge base. See our Custom GPT and LLM solutions page for the LLM-specific offering.

Document AI and intake automation. Loan applications, prior-auth forms, RFPs, invoices, and transcripts extracted into structured fields with confidence scores, plus human review queues for anything below threshold.

Integrations to core systems. Symitar, Corelation, Epic, Cerner, Procore, Deltek, Workday, Ellucian Banner, Salesforce, and the middleware in between. AI without integration is a demo.

Industries we develop custom AI for

Credit unions on loan processing, fraud, member service, and NCUA-aligned governance. Details on AI consulting for credit unions.
Healthcare organizations on documentation, prior authorization, and patient flow, with HIPAA-appropriate data handling. Details on healthcare AI consulting.
AEC firms on bid analysis, submittal review, and project risk scoring. Details on AEC AI consulting.
Higher education on enrollment, retention, and administrative automation, with FERPA in scope. Details on higher education AI consulting.

How our custom AI development engagement works

Every engagement follows the same phases, priced fixed-fee per phase so you can stop after any of them.

Assessment (1 to 2 weeks). We interview the people who will use the system, map the workflow, quantify the current cost, and confirm the data is usable. Deliverable: a written scope, price, and expected payback.
Prototype (2 to 4 weeks). A working system on a subset of real data, evaluated against a labeled test set your team helps build. Deliverable: measured accuracy on your data and a go or no-go decision.
Production build (4 to 10 weeks). Integration into your systems, security review, monitoring, retraining pipeline, and change management. Deliverable: a running system your team owns.
Operate and improve (ongoing, optional). Model monitoring, drift alerts, quarterly retraining, and enhancement work. Priced monthly.

Most projects finish phases 1 through 3 in 8 to 16 weeks. Complex integrations across multiple core systems or heavy compliance review run 16 to 24 weeks.

How to evaluate a custom AI solution before production

Define an acceptance test. Use real examples from the target workflow, including exceptions. Agree which outputs need human approval and what failure should look like.
Check access and traceability. Enforce source-system permissions, record the evidence used for an answer, and retain the logs your team needs to investigate it.
Prove the integration. Test the full path from intake to the destination system, including duplicate requests, unavailable services, and a rollback procedure.
Measure operating cost. Track model usage, review time, and support effort alongside hours saved. Compare those results with the licensed tools you already own.
Name the owner. Document who handles incidents, approves model changes, and maintains the system after handover.

Frequently asked questions about custom AI solutions

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Ready to scope a specific custom AI project?

A first call is a 30-minute scoping conversation, not a sales pitch. Bring one workflow you want to fix. We will tell you whether a custom AI build is the right answer, what it will cost, and how long it will take.

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