AI development
Custom product features and operational software built around a specific job.
AI Development / Integration / Consulting
AI development that fits the way your business works.
F2 designs, builds, and connects AI systems to the data, software, and workflows your team already uses. Start with a clear job, put controls around it, and measure what changes.
What F2 handles
Consulting, development, and integration stay connected.
F2 works across assessment and implementation. We can define the use case, design the system, build the software, connect it to the existing stack, and stay close enough to see how it behaves in real work.
Frank stays directly involved, so technical choices remain tied to the business problem they are meant to solve.
AI integration
Models connected to the data, permissions, applications, and actions around the workflow.
AI consulting
Use-case selection, architecture, risk decisions, vendor evaluation, and a practical build plan.
Common use cases
AI systems for defined operating tasks.
The useful unit is not a chatbot or a model call. It is a complete path from trusted context to a reviewable action.
Knowledge search
Search and answer experiences grounded in approved manuals, policies, product data, and client records, with citations back to the source material.
Document workflows
Classify incoming files, extract structured fields, draft outputs, and send low-confidence cases to a person before anything moves downstream.
Lead routing
Summarize inquiries, check fit against explicit rules, update the CRM, and route each lead with the context needed for a useful follow-up.
Internal assistants
Help teams find procedures, prepare account notes, draft responses, and assemble next steps inside the tools they already use.
Product features
Add search, recommendations, natural-language controls, structured generation, or domain-specific assistance to an existing product.
Reporting
Combine campaign, sales, and operational data into recurring summaries, flagged exceptions, and reports that link back to the underlying numbers.
Architecture
The model is one component, not the whole architecture.
A production integration needs clear inputs, an explicit control layer, and a defined path into the business workflow. Each boundary can be inspected, tested, and changed without treating the model as the source of truth.
Sources
Approved business context
Documents, CRM records, product data, analytics events, and written business rules.
- Authoritative records
- Freshness requirements
- Ownership and retention
Control layer
Logic around the model
Retrieval, instructions, validation, identity, permissions, model selection, and confidence handling.
- Role-aware access
- Structured outputs
- Limits and fallbacks
Review and action
A safe path into the workflow
Human approvals, application updates, alerts, audit logs, and clear behavior when the system is uncertain.
- Review queues
- System updates
- Traceable decisions
Delivery safeguards
Controls belong in the first build.
Data, permissions, human review, testing, and measurement are design inputs. They are not cleanup work for later.
Data
We identify which records are authoritative, how current they need to be, who owns them, and what should never enter the system.
Included in delivery
Source map + handling rules
Permissions
We design around existing access rules, scope credentials, and test behavior for each relevant role.
Included in delivery
Access matrix + boundary tests
Human review
Outputs that meet agreed impact or uncertainty thresholds are routed for review. A person can inspect the source context, edit the result, or override it.
Included in delivery
Review thresholds + escalation path
Testing
Evaluation sets use representative inputs, edge cases, and known failure modes. We test quality, permissions, latency, cost, and fallbacks.
Included in delivery
Evaluation set + release criteria
Measurement
The system gets a baseline and a focused set of operating metrics. We inspect live behavior and make changes from evidence.
Included in delivery
Baseline + operating dashboard
How the work runs
From a scoped use case to a monitored production system.
Scope expands only after the core path works, the boundaries hold, and the team can see what the system is doing.
Define the job
Name the user, input, decision, action, and useful outcome. Narrow scope makes evaluation possible.
Map the environment
Document the systems, data, permissions, owners, risks, and integration constraints around the work.
Build a thin slice
Create one complete path with real examples so the core assumptions can be tested before expanding.
Integrate the controls
Add identity, data access, validation, human review, logging, monitoring, and fallback behavior.
Release and observe
Launch to a defined group, compare against the baseline, inspect failures, and decide what earns a wider rollout.
AI integration FAQ
Questions worth answering before the build.
The right technical approach depends on the workflow, the source systems, and the cost of being wrong.
What is the difference between AI consulting and AI development?
AI consulting defines where the technology fits, what data and controls it needs, and how the work should be measured. AI development turns that plan into working software. F2 handles both, so strategy, architecture, integration, and implementation stay connected.
Can you integrate AI with software we already use?
Often, yes. We first map the available APIs, webhooks, databases, authentication, and workflow constraints. The design then works with those boundaries instead of assuming every system can be connected in the same way.
How do you protect business data and existing permissions?
We scope data sources and credentials, preserve role-based access where the source systems support it, minimize retained data, and document how information moves through the integration. The exact controls depend on the systems and model services selected for the project.
How do you test an AI feature before launch?
We create an evaluation set from representative inputs, edge cases, and known failure modes. Testing covers output quality, access boundaries, structured data validation, latency, cost, and the behavior of review and fallback paths.
Does every AI workflow need a human reviewer?
No. Review requirements should match the consequence, uncertainty, and reversibility of the action. High-impact decisions and low-confidence outputs can stop for approval, while routine and reversible work can run with logging and clear exception handling.
How do you measure whether an AI integration is working?
We establish a baseline before release and define a small set of operating metrics for the use case. These may include completion rate, review rate, error rate, time to resolution, user adoption, and cost per task. The live system supplies the evidence.
Where should an AI project start?
If the use case is already clear, start with a focused technical discovery for that workflow or product feature. If the opportunity is still broad, the AI Readiness Assessment can identify and rank the most practical places to begin.
A practical next step
Put one defined AI workflow into production.
Bring the workflow, data source, product feature, or operational bottleneck. F2 can help define the right scope, build the integration, and set up the controls and measurement around it.