AI integration services
AI Integration for Your Products and Business Workflows
Connect AI features and automation to the software your business already uses. Work directly with Bill Vivino on integration, data access, evaluation, human review, and rollout.
Working Alongside Your Team
Start with one task in the software your team already uses: finding an answer, preparing a draft, summarizing a record, or routing work for review. Bill can own the agreed integration alongside your business owner and engineers, connecting the feature to your application, identity system, and approved data.
Start with the task your team needs to complete and the systems it already uses. Agree the decision owner, data access, success measures, and when a person must review the result. AI may be part of the answer; conventional automation or a clearer software workflow may be the better choice.
AI Integration Consulting for Existing Products and Workflows
MCP Integration Sprint
Connect one internal system through the Model Context Protocol (MCP), a standard way for AI tools to connect to systems. Scope a working workflow, permission tests, and an operating guide around narrowly scoped tools, user permissions, approval steps, and audit logs.
AI Production Safety Layer
Build repeatable evaluations for prompt injection, data access, tool permissions, failure handling, and human escalation. Add action limits, deployment checks, and incident logs around an existing agent workflow.
High-Assurance Workflow Review
Map how sensitive requests are verified before data is released or actions are taken. Review request provenance, independent identity checks, dual approval, policy rules, and anomaly detection, then prioritize control gaps.
Due-diligence and vendor-risk automation
Connect approved sources to research and review queues for legal, procurement, fintech, and compliance teams. Scope traceable evidence, source-linked summaries, exception handling, and human decision checkpoints around one agreed workflow.
AI FinOps & ROI Review
Measure inference, infrastructure, retries, latency, and human-review costs per successful task. Review a cost baseline, provider comparison, possible savings, and criteria for continuing or stopping the workflow.
AI feature in an existing app
Add AI assistance to a mobile app, web app, portal, or dashboard. Include the existing product behavior, identity, APIs, and data-access boundaries in the implementation and evaluation plan.
Relevant AI Work
Applied visual computing
Experience includes native vision features, browser-based segmentation, image-generation workflows, and server-side motion analysis. The appropriate technique depends on the product's task and data.
View ProofInternal coding AI prototype
A consulting-intake prototype explores local models, retrieval, evaluation, rate limits, and human handoff. It is an internal example, not a client deployment or a measured business-outcome case study.
View ProofAI Assisted Development and Workflow Automation
Product fit and data readiness
Identify where AI should help, where deterministic software is safer, and what humans still need to review. Check source quality, ownership, access, and gaps before committing to an implementation.
AI architecture
Design the data flow around model calls, retrieval, citations, permissions, storage, logging, rate limits, evaluations, routing rules, and backend APIs.
Implementation support
Build AI-backed features inside existing mobile apps, web apps, internal tools, portals, and operational systems.
Prototype evaluation and stabilization
Evaluate an existing AI prototype and address gaps in prompts, context, application structure, testing, and failure behavior.
Provider-portable architecture
Keep business rules separate from model adapters. Compare OpenAI, Anthropic, and open models against shared evaluations, with workload routing, cost telemetry, and tested fallbacks suited to the product.
Retrieval and evidence quality
Connect approved documents and structured data, preserve source links, and test retrieval quality, access boundaries, stale information, and unsupported claims before results reach reviewers.
Operating visibility and controls
Make usage, failures, response times, model costs, and human-review effort visible. Define deployment checks, action approvals, recovery steps, and who owns monitoring and escalation.
User adoption and handoff
Include the people doing the work in demonstrations and feedback. Agree how they will use the workflow, where they can override it, what training is needed, and who supports it after handoff.
Questions About Adding AI to Your Business Software
How can we add AI to our existing business system?
Start with one workflow and connect it through the system's existing APIs, permissions, and data sources. Define the input, useful output, and review step before selecting a model. A first implementation could assist with search, drafts, or summaries while keeping the current application responsible for access and business rules.
Do we need to rebuild our software first?
Often, no. A defined integration may fit the current application. First review its interfaces, data quality, and access controls. If those foundations are missing, scope the necessary backend work before the AI feature. Conventional automation may be enough when the task follows predictable rules.
How do we handle private data and AI actions?
Agree the allowed data sources, user permissions, provider or hosting constraints, and retention requirements before implementation. Keep access checks in the application and limit the actions the AI can request. Identify changes that need human approval and test denied access and failure paths as well as successful requests.
Can you improve an AI prototype we already have?
Yes. Review representative successes and failures, retrieval, prompts, permissions, and application behavior. Separate an impressive demo from a workflow your team can use. The first scope can address a specific failure and produce evaluation results, known limits, and a release recommendation.
How will we know whether the pilot is useful?
Agree examples and a baseline before building. Compare task completion, answer quality, human-review time, exceptions, and operating cost. Use those results to decide whether to expand, revise, or stop. Accuracy, savings, and adoption are outcomes to evaluate, not promised results.
Can you work with our engineers and support rollout?
Yes. Define the integration Bill owns, the decisions your team owns, and the release checks. Include an operating guide, escalation owner, and handoff in the scope. Further implementation, training, and support are agreed explicitly after the pilot review.
Related Services
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Technical leadership for AI strategy, architecture, vendor decisions, and product planning.
View ServiceHealthcare Software Consultant
Healthcare workflow consulting where AI may support intake, triage, search, or documentation.
View ServiceSenior Software Consulting
Senior consulting for apps, AI features, MVPs, and codebase rescue.
View ServiceWhat Workflow Should Work Better?
Bring one workflow and the result or decision you need. Share what exists today, where the work gets stuck, which systems and data are involved, and who owns the decision. We can agree an appropriate first consulting step.
Discuss Your AI Workflow