AI-assisted internal tools
Custom internal applications that help staff search, draft, classify, route, and complete work, with role-based permissions, audit logging, and review checkpoints engineered into the workflow.
What We Do
We're a custom software development firm that develops AI applications
We design and develop AI applications, integrations, and workflows for organizations that need production software connected to real business systems. We work with credit unions, community banks, fintechs, healthcare organizations, insurance companies, associations and member organizations, SaaS and product companies, and operations-heavy mid-market businesses. Alongside our custom development work, we also develop AI products in-house, including EURIQA, our applied-AI product.
In one sentence
We are a custom software development firm. AI is one of the application layers we design, integrate, deploy, and support inside real operating environments.
Definition
What AI implementation means
AI implementation goes beyond choosing a model or writing prompts. It is the engineering work of turning AI capability into working software inside real workflows.
That work includes architecture, integrations, data pipelines, permissions, user interfaces, evaluation, audit logging, deployment, monitoring, and the production support that runs after launch. It is the same discipline that goes into any production software, applied to applications that include AI. We connect AI to your existing workflows and systems of record so your team can actually use it in production.
The core idea
AI strategy decides what to do. AI implementation ships the working software. We handle the implementation: architecture, integration, security, deployment, and ongoing support.
What this looks like in practice
- Connecting AI to your data, your tools, and your APIs
- Designing where humans review and where automation runs
- Setting access boundaries before AI touches anything
- Shipping production-ready software connected to real workflows
Capabilities
What we implement with you
Production AI applications, integrations, and workflows engineered for the systems and compliance requirements your organization already operates inside.
Staff copilots
AI copilots embedded in operations, member service, claims, lending, benefits, and back-office workflows, with role-based permissions, audit logging, and human approval gates connected to your systems of record.
Workflow automation
Multi-step automations that move work between people, systems, and AI components through APIs and MCP servers, with logging, approval steps, and clear handoffs between human and machine work.
Knowledge search and RAG
Retrieval-augmented generation built on your approved documents, policies, and internal data, with source citations, permission-aware access, and version-controlled knowledge sources. Common starting point: turn your existing FAQs and member documentation into an AI assistant that answers questions from your data.
Document intake and processing
OCR, classification, extraction, and summarization for benefit plans, claims, applications, contracts, and compliance documents, with structured outputs that route into existing systems through APIs and data pipelines.
Customer and member support workflows
AI-assisted support for member, customer, and patient interactions, with escalation paths, brand-safe responses, audit logging, and clear handoff points to staff before anything reaches a member or customer.
Reporting and decision support
AI features that summarize, analyze, and surface what matters from operational data, paired with human review for sensitive decisions, evaluation harnesses, and audit-ready output trails.
MCP servers and system integrations
Model Context Protocol servers and API integrations that give AI models controlled, auditable access to internal tools, data sources, and systems of record, scoped by role and permission with full audit logging.
AI features inside existing products
AI capabilities embedded into web, mobile, and SaaS applications, including search, recommendations, generation, and automation, designed to feel native to the product they live inside, with monitoring designed in from day one.
Secure data-connected AI applications
Custom applications that connect AI models to internal data, systems of record, and member-facing surfaces with the security posture, data residency, and access controls your organization requires.
Evaluation and monitoring
Production evaluation harnesses, drift monitoring, prompt regression testing, and quality thresholds tracked against defined acceptance criteria so quality is measured against numbers your team can see.
Human-in-the-loop workflows
Review checkpoints, approval gates, and escalation paths designed into the workflow so important outputs always pass through a person, with the audit trail an examiner expects.
Why a Custom Software Firm
AI features work inside applications and workflows
Production AI is integrated with systems of record, accessible through user interfaces, governed by permissions, and supported by the engineering practices that keep production software running. That engineering layer is what we do.
Software architecture
Application architecture for AI features that scale, stay observable, and remain operable across model and provider changes.
APIs
Designed and versioned APIs between AI components, your internal systems, and the user interfaces staff and members actually use.
Integrations
Integration work across modern and existing systems of record, including cores, AMS systems, EHRs, claims platforms, and benefits administration systems.
Data workflows
Pipelines, transformations, embeddings, and movement that get the right data to the AI and the right outputs back to the systems that need them.
Permissions
Role-based access enforced at the system layer so AI only sees what the user is allowed to see, with the controls auditors and examiners expect.
Security
Security practices for regulated industries, including authentication, encryption, key rotation, and isolation between environments.
UX
Interfaces that real teams want to use, with the visibility, edit affordances, and review checkpoints production AI needs to be trusted.
Adoption
Rollout, training, documentation, and the kind of operational support that helps AI features get used after launch, not parked.
Regulated industries
Years of experience shipping software for credit unions, banks, healthcare organizations, insurance, associations, and member-driven organizations.
Production deployment
Deployment, environment management, and release practices that get AI software live without surprising the people who run the rest of your stack.
Long-term maintainability
Version-controlled prompts, infrastructure as code, documented architecture, and clean handoffs so the system stays operable across years, not weeks.
Implementation Approach
From workflow analysis to working production software
A clear, repeatable five-step approach. Each step ends with something you can review, decide on, and act on.
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Identify the right use case
We start with the workflows where AI assistance creates real value for staff, members, customers, or operations. We rank candidate use cases by impact, feasibility, and complexity.
You get: a prioritized use case shortlist with expected impact, technical considerations, and a recommendation on where to start.
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Map systems, data, and workflows
We document the systems of record, data sources, permissions, and existing workflow steps the AI needs to integrate with. We confirm what is accessible, what is sensitive, and where review needs to happen.
You get: a system and integration map, a data and permissions review, and an architecture sketch showing how AI fits into existing software.
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Prototype the highest-value workflow
We build a focused prototype around the strongest use case so your team can validate the workflow, test the experience, and confirm the approach before broader investment.
You get: a working prototype connected to representative data, a quality evaluation against defined criteria, and a recommendation on whether and how to proceed.
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Develop production-ready software
We engineer the application around the AI: integrations, permissions, evaluation, audit logging, and the user interface staff will actually use day to day.
You get: production-ready software with documented architecture, security review, evaluation harness, and a deployment plan.
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Integrate, deploy, measure, and improve
We deploy into your environment, connect remaining systems, monitor outputs against defined quality thresholds, and iterate as workflows mature and models change.
You get: a deployed AI application with monitoring, alerting, an improvement plan, and the documentation your team needs to operate it.
Productized Entry Engagement
AI Workflow Discovery Sprint
A scoped, deliverable-driven first engagement when you're ready to identify and plan your first AI implementation. The sprint runs 4 to 6 weeks and gives you everything you need to decide how to proceed.
What you get
- Use case prioritization across candidate workflows
- Workflow and system mapping for the top use cases
- Technical feasibility review against your stack and integrations
- Data and permissions review for the workflows in scope
- Security considerations specific to your environment
- Implementation roadmap with sequencing and dependencies
- First build recommendation with planning range and scope
The Discovery Sprint gives your team a real plan and a clear recommendation. If a smaller engagement is the better fit, we'll tell you. If a different starting point would set your team up better, we'll tell you that too.
Industry Use Cases
Practical AI implementation by industry
Job-to-be-done use cases drawn from regulated and operations-heavy environments where production software, real data, and review processes already exist.
Credit unions and community banks
- Help member service teams search policies, products, and procedures faster, with answers grounded in approved documentation
- Process loan documentation, applications, and member-facing forms with extraction, classification, and routing for staff review
- Surface fraud signals and risk indicators alongside existing tooling, with audit trails for examiners
- Draft and route member communications with version-controlled templates and human approval gates
- Add AI assistance to internal tools without giving the AI direct access to the CORE
Healthcare
- Process clinical documentation and summarize patient records for staff use
- Develop HIPAA-aware knowledge tools for clinicians and administrators
- Add AI to existing patient or staff portals with audit trails and review checkpoints
- Support claims, eligibility, and benefits workflows with document understanding
- Embed AI features in existing healthcare software with appropriate data handling
Insurance and risk pools
- Review and extract data from policies, claims, and applications
- Add AI assistance to underwriting and claims workflows with human approval gates
- Develop agent-facing copilots that respect carrier and reinsurer data boundaries
- Automate policy and notice generation with version control
- Support compliance documentation and audit-ready logging
Associations and member organizations
- Help members find answers across approved content and member benefits
- Process documents, applications, and renewals with AI assistance
- Add AI features to member portals and self-service tools
- Automate communications where the work is repetitive and review is owned by a person
- Connect AI to existing AMS systems through secure integrations
SaaS and product companies
- Embed AI features inside existing products that customers already use
- Add copilots, search, summarization, and workflow automation as first-class features
- Connect to your customers’ data through secure MCP servers and APIs
- Design evaluation and monitoring into AI features from day one
- Support enterprise customer requirements around data residency and access
Internal operations teams
- Add AI assistance to RFP intake, proposal review, and response drafting
- Speed up reporting, document review, and recurring back-office work so staff can focus on higher-value tasks
- Develop internal copilots that connect to the systems your operations team already uses
- Add knowledge search across approved policies, procedures, and runbooks
- Keep human review where the important decisions actually live
Logistics and operations-heavy companies
- Process intake documents, BOLs, and dispatch records with extraction and routing
- Add AI assistance to scheduling, exception handling, and back-office workflows
- Surface delays, anomalies, and operational signals from existing operational data
- Connect AI to TMS, WMS, and ERP systems through APIs and MCP servers
- Keep human review where dispatch and exception decisions actually live
Governance, Security, and Practical Risk
AI works best when access, permissions, and review are designed intentionally
The strongest AI applications are the ones with the clearest boundaries. We design AI software around what the system can access, what it can do, and where a person needs to sign off. The same engineering rigor that goes into compliance-sensitive software goes into the AI that runs inside it.
Permissions
AI sees what the user is allowed to see. Role-based access enforced at the system layer, not just inside the prompt.
Data boundaries
Sensitive data, including PHI, member information, and confidential business data, stays inside the systems and regions you require.
Audit logging
Every AI action recorded so your team can review, debug, and demonstrate compliance to auditors and examiners.
Human review checkpoints
Consequential outputs route to a person before they ship. Review gates designed into the workflow from day one.
Secure integrations
MCP servers, APIs, and middleware built with the same security posture your production systems require, with authentication, encryption, and key rotation.
Model and vendor selection
Models and providers chosen based on data sensitivity, latency, accuracy, and contractual requirements. The choice is documented and revisitable.
Monitoring
Production monitoring for drift, regressions, latency, cost, and quality, with alerts when behavior moves outside defined thresholds.
Evaluation
Evaluation harnesses against representative inputs so quality is measured, not assumed. Regression tests run when models or prompts change.
Maintainability
Version-controlled prompts, infrastructure as code, documented architecture, and clean handoffs so the application stays operable over time.
Prompt-injection considerations
Architecture and input handling designed to resist users coaxing the AI into exposing data or taking unauthorized action.
Access controls
Credentials handled the way any production system handles credentials, with scopes, rotation, and isolation between environments.
Compliance posture
HIPAA-aware data handling, audit-ready logging, and security practices that align with the regulatory frameworks your organization operates inside.
Engagement Bands
Planning ranges and timeframes
Four common engagement shapes. Each one pairs a typical timeframe with a planning range. Final scope is set after an initial technical conversation.
| Engagement | Best fit | Typical timeframe | Planning range | Typical outputs |
|---|---|---|---|---|
| AI Workflow Discovery Sprint | When you're ready to identify and scope the right first AI use case | 4 to 6 weeks | $15,000 to $25,000 | Use case roadmap, system map, feasibility review, implementation plan |
| Single-use-case implementation | When you have a defined use case ready to ship to production | 6 to 8 weeks | $25,000 to $45,000 | Production-ready application, integration layer, evaluation harness, deployment |
| Multi-system implementation | Larger AI initiatives connecting multiple internal systems | 8 to 16 weeks | $45,000 and up | Multi-system AI application, integrations, governance, monitoring, training |
| Custom AI agents | Agentic systems with bounded scope, integrations, and governance | 8 to 24 weeks | $50,000 to $300,000, depending on integrations and compliance scope | Production agent application, MCP and API layer, evaluation, audit logging, monitoring |
Actual scope depends on system access, vendor coordination, data complexity, testing requirements, compliance posture, and implementation path. Project estimates are scoped after an initial technical conversation.
After Your First Call
What happens after your first call with us
The first conversation is technical, specific, and useful. You leave with a written summary, a clear next step, and a realistic sense of what it would take to ship.
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During the conversation
We talk through the workflow you want to improve, the systems involved, the data sources at play, and what success looks like for your team.
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What we review
Likely architecture, integration points, data and permission considerations, the kind of review checkpoints we would design in, and anything worth flagging early.
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What you get afterward
A written summary, a recommended next step, and a sense of the engagement shape and planning range that would fit the work.
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What the next step looks like
If a Discovery Sprint is the right fit, we scope it. If a smaller engagement makes more sense, we say so. If a different starting point would set your team up better, we say that too.
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Who should join from your side
A senior operator who knows the workflow, a technical lead who understands the systems involved, and a decision-maker who can act on the recommendation.
Frequently Asked Questions
Common questions about AI implementation
What is AI implementation?
AI implementation is the engineering work of turning AI capability into working software inside real workflows. That includes architecture, integrations, data pipelines, permissions, user interfaces, evaluation, audit logging, deployment, and the production support that runs after launch. It is the production-ready version of an AI idea.
How is AI implementation different from AI consulting?
AI consulting typically produces strategy, recommendations, and roadmaps. AI implementation produces working software. We're a custom software development firm. We design, integrate, develop, deploy, and support the application. Strategy work is part of the engagement when it is needed, but the deliverable is production software.
Can you add AI to existing software?
Yes. Most of our AI implementation work extends software that is already in production. That includes adding AI features to existing web, mobile, and SaaS applications, developing copilots inside internal tools, and integrating AI with systems of record like CORE banking platforms, AMS systems, EHRs, claims systems, and benefits administration platforms.
Do we need clean data before starting?
Not always. Many useful AI implementations work fine on the data your organization already has. The Discovery Sprint includes a data and permissions review so we can be straightforward about what's ready to use today, what would benefit from preparation work first, and what data quality work would unlock additional use cases later.
Can AI be implemented safely in regulated industries?
Yes, when the application around the AI is engineered correctly. We design for HIPAA-aware data handling, role-based access, audit logging, secure integrations, and human review checkpoints for any sensitive output. The same engineering rigor that goes into compliance-sensitive software goes into the AI that runs inside it.
What kinds of AI tools can you develop?
AI-assisted internal tools, staff copilots, workflow automation, knowledge search and RAG applications, document intake and processing, customer and member support workflows, reporting and decision support, MCP servers and system integrations, AI features inside existing products, and custom AI agents with bounded scope, governance, and evaluation.
How long does an AI implementation project take?
An AI Workflow Discovery Sprint runs 4 to 6 weeks. A single-use-case implementation runs 6 to 8 weeks. Multi-system implementations run 8 to 16 weeks. Custom AI agent engagements run 8 to 24 weeks depending on integrations, governance, and compliance scope. Compressed timelines are possible when scope is clear and access is ready.
What does AI implementation cost?
Discovery Sprints typically run $15,000 to $25,000 over 4 to 6 weeks. Single-use-case implementations run $25,000 to $45,000 over 6 to 8 weeks. Multi-system implementations start at $45,000 and scale with scope over 8 to 16 weeks. Custom AI agents typically run $50,000 to $300,000 over 8 to 24 weeks depending on integrations and compliance posture. Final estimates are scoped after a technical conversation.
Can you help identify the right first use case?
Yes. The AI Workflow Discovery Sprint is designed for that. We map candidate use cases against impact, technical feasibility, data and permission requirements, and complexity, then recommend a first build. Many engagements start that way because picking the right first use case is what sets the project up to ship cleanly and deliver real value.
Do you work with existing systems of record?
Yes. We have 17 years of experience integrating with systems of record across credit unions, banks, healthcare organizations, insurance companies, associations, and SaaS products. AI implementation work commonly connects to CORE banking systems, AMS systems, EHRs, claims platforms, benefits administration platforms, CRMs, and internal data warehouses through APIs, MCP servers, and middleware where appropriate.
Phone: (503) 468-4880
Email: connect@buildableworks.com
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