AI readiness and custom pilots for teams that need practical operational leverage
Start with the workflow, not the hype. FC Tech Labs helps service businesses and regulated teams identify where AI is worth using, scope the right first pilot, and deliver it with governance and operational discipline.
- Cloud operations and infrastructure depth that keeps recommendations grounded in real delivery constraints.
- Compliance-aware delivery with eval plans, governance checklists, rollout gates, and traceable decisions.
- Controlled pilots that can move into daily operations with measurement, ownership, and rollback paths.
Assessment first. Pilot second. Delivery grounded in cloud operations, governance, and measurable business value.
What we emphasize
- Evidence and governance first; shipping with measurement, not experiments.
- Scoped engagements with clear boundaries and artifacts ready for audits.
- Security/Compliance involvement from week one, not after launch.
Current focus
Service businesses, operations-heavy teams, and organizations with low internal technical capacity. Founder-led delivery; a handful of concurrent engagements only.
Business-first outcomes
Outcomes that move operations forward
Reduce cycle time without increasing risk
Ship faster by tightening SDLC guardrails, evidence capture, and review clarity instead of adding manual steps.
Ship AI with evals, guardrails, and audit trails
Evaluation plans, red-team paths, and regression gates wired into CI/CD so every release is explainable.
Prevent shadow AI sprawl with governance
Data boundaries, access maps, and approval paths that make Security/Compliance allies, not blockers.
Make security and compliance stakeholders allies
Shared artifacts and clear roles so Security/Compliance can sponsor the rollout instead of slowing it down.
Evidence-driven, practical
Applied AI and modernization practice
Integrating AI into daily operations demands evaluation-first thinking. We focus on evidence, governance, and usable workflow design so modernization work creates leverage instead of more process debt.
AI fails inside small and mid-sized businesses when outputs are unmeasured, ungrounded, or disconnected from the way teams already work. Our delivery approach responds to operations, security, and leadership concerns: define the workflow, set boundaries, test against real examples, and only ship improvements that show measurable business value.
Research focus areas (applied)
Evidence-grounded AI responses
Reduce hallucinations by tying AI outputs to approved sources, retrieval boundaries, and reviewable citations.
Human-in-the-loop workflow design
Keep people responsible for high-risk judgment while AI handles drafting, summarizing, routing, and checking.
AI as a complement to existing systems
Layer AI onto the tools teams already use instead of forcing a disruptive platform switch too early.
Repeatable evaluation harnesses
Golden examples, logged prompts, and acceptance thresholds make AI behavior testable before rollout.
Operational consistency
Track stability across workflow changes so teams can trust the system under normal business pressure.
Human-in-the-loop validation
Experts arbitrate high-risk calls; abstentions are allowed when evidence is weak.
How we evaluate AI systems
- Controlled, blinded evaluations anchored to expert human baselines.
- Outputs judged on correctness, evidence quality, actionability, and hallucination presence.
- Abstaining is acceptable—and measured—when evidence is insufficient.
Baselines and comparators
Existing manual steps, current tool outputs, and known edge cases become the baseline. The goal is not to replace judgment; it is to prove where AI can safely reduce drag, improve consistency, or speed up a handoff that already costs the team time.
Metrics that matter
Hours saved
Shows whether the workflow is worth automating.
Cycle time
Measures how quickly work moves from intake to completion.
Error and rework rate
Signals quality, trust, and operational risk exposure.
Escalation clarity
Keeps human review focused on the moments that need judgment.
Evidence completeness
Makes recommendations defensible for leadership, security, and compliance.
Adoption friction
Shows whether the team can actually use the workflow after launch.
Security and compliance alignment
FC Tech Labs treats data boundaries, access control, vendor risk, and approval paths as part of the work. That keeps AI adoption credible for leaders who need business value without creating avoidable exposure.
How this informs consulting delivery
Consulting engagements reuse the same operating artifacts: workflow maps, risk registers, evaluation plans, rollout checklists, and evidence packs. The result is implementation work that can be understood by operations, security, and executive stakeholders.
Adoption guidance
- Use the team's existing systems where possible before proposing a custom platform.
- Keep interfaces simple enough for non-technical operators to trust and repeat.
- Train around the workflow change, not just the AI feature.
Guardrails
- No compliance certifications claimed.
- No funding agencies named unless later approved.
- No guarantees implied.
- Do not automate sensitive decisions without a clear human review path.
Fit matters
Who this is for—and who it isn’t
For teams that want
- COOs, Operations Leaders, and CTOs with costly manual workflows
- Service businesses that need a practical first AI use case
- Teams that want a clear path from readiness assessment to pilot
- Organizations that can put workflow owners in the room for a focused engagement
Not the right fit if
- “We just want a chatbot” without real workflows
- No access to SMEs or workflow owners
- No appetite for process change or measurement
- Speculative experiments without production intent
Choose the right entry point
Engagements (fixed scope)
1–2 weeks
AI Readiness & Risk Assessment
Leaders who need a clear go/no-go plan before committing engineering time.
What we do
- Map data boundaries, PII/PHI/PCI handling, and access paths.
- Assess SDLC, CI/CD, and change-management readiness for AI workloads.
- Identify quick wins vs. high-risk areas with ROI x risk scoring.
Deliverables
- AI Opportunity Map with ROI x risk matrix
- Data Boundary Diagram with access paths and controls
- Risk register + mitigations prioritized by engineering effort
2–4 weeks
Automation Quick Wins Sprint
Teams that want measurable automation in production with low blast radius.
What we do
- Select 1–2 high-friction workflows and define success metrics.
- Build guarded automations with evals and rollback hooks.
- Ship to a limited GA with observability and evidence capture.
Deliverables
- Automation spec + acceptance criteria
- Evaluation Plan + regression gates wired to CI/CD
- Rollout Plan with pilot → limited GA → GA
4–10 weeks
Production AI Implementation
Teams ready for production AI with governance and auditability baked in.
What we do
- Design and implement AI workflows with evals, guardrails, and fallbacks.
- Integrate with your CI/CD, observability, and incident response.
- Run readiness reviews with Security/Compliance/Legal and document approvals.
Deliverables
- Model/System Card with monitoring and fallback paths
- E2E evaluation harness + golden sets
- Evidence pack for Security/Compliance sign-off
1–3 weeks
Governance & Evidence Pack
Teams with a pilot who need audit-ready artifacts and stakeholder alignment.
What we do
- Document controls, approvals, and change-management for AI features.
- Build evidence pack for auditors and security reviewers.
- Define ongoing eval + monitoring cadence with owners.
Deliverables
- Governance checklist with owners and approval paths
- Evidence pack (screenshots/exports/log excerpts) ready for audits
- Runbook for change management and regression handling
Evidence-first artifacts
Deliverables you can show your board, auditors, and security team
AI Opportunity Map
ROI x risk matrix that prioritizes where to apply AI without breaking controls.
Data Boundary Diagram
PII/PHI/PCI zones, access paths, and control points auditors can trace.
Evaluation Plan
Golden sets, thresholds, and regression gates that protect production quality.
Model/System Card
Purpose, limitations, monitoring, and fallbacks—ready for security review.
Governance Checklist
Policy, approvals, and change management mapped to owners and evidence.
Rollout Plan
Pilot → limited GA → GA with success metrics and rollback hooks.
4-step, evidence-first
How we work
Diagnose
Workflows, data boundaries, and control points. Stakeholder alignment: Eng + Security + Compliance.
Evidence: Risk register, data boundary map, go/no-go criteria.
Design
Guardrails, eval plan, and rollout gates. Cloud-agnostic with AWS depth; GitHub/GitLab interchangeable.
Evidence: Evaluation Plan, acceptance criteria, rollback and escalation paths.
Deliver
Build and ship scoped work in production with observability and change-control tickets.
Evidence: Audit-ready change log, deploy notes, linked evidence exports.
Prove
Measure impact, regressions, and control adherence. Hand off artifacts your board/auditors can inspect.
Evidence: Before/after metrics, evidence pack, monitoring handoff.
Selected outcomes
Proof
Who is behind the work
Every engagement is led by Rafael Fernandez — who deployed and governed AI products at a major US financial institution and led an early IBM Granite rollout in 2022. You work directly with the principal, not a junior consultant.
Read the full background →Founder experience that backs every engagement
- Deployed, secured, and governed AI products at a major US financial institution — production ownership, not advisory.
- Led an enterprise IBM Granite rollout to internal development teams in 2022, before mainstream enterprise AI adoption existed.
- Cloud operations and platform engineering at scale: AWS, Terraform, Kubernetes, CI/CD, and compliance-aware infrastructure.
- Translate real AI governance pressure — data boundaries, security sign-offs, audit trails — into practical consulting deliverables teams can use.
What you’ll get after week 1
- Workflow + data boundary map aligned with Security/Compliance.
- Preliminary evaluation plan with golden set candidates and success metrics.
- Risk register with owner alignment and next-step implementation plan.
Premium, limited
Retainer option: Fractional AI Architecture & Governance
Ongoing advisory for regulated teams that want continuity without hiring a full-time lead. Limited hours per month with prioritization for architecture reviews, risk reviews, and governance upkeep.
Included
- Eval maintenance and drift reviews
- Roadmap and design reviews with Security/Compliance
- Quarterly evidence pack refresh
- Incident/rollback guidance with playbooks
Constraints
- Limited monthly hours; prioritized backlog
- Typical response: 1–2 business days (confirm SLA with Rafael)
- Does not replace internal change control or approvals
Straight answers
FAQ
Are you cloud-agnostic?
Yes. We design for AWS, GCP, or Azure. We have deep AWS experience; patterns stay cloud-agnostic.
Do you build or advise?
Both. We ship scoped work and leave artifacts, runbooks, and guidance your teams can reuse.
How do you handle data privacy and regulated data?
Data boundary mapping, least-privilege integration, and explicit PII/PHI/PCI handling. No data persistence outside your controls.
What models do you use?
Whatever best fits the risk, latency, and cost constraints—foundation models, hosted APIs, or smaller models with evals.
How do you prevent hallucinations or errors?
Guardrails + evals tied to golden sets, regression gates in CI/CD, and monitored fallbacks with clear ownership.
How do you measure ROI?
We define success metrics per workflow (cycle time, error rate, evidence time saved) and report before/after with controls.
Can you work with our security/compliance teams?
Yes. We include Security/Compliance in scoping, reviews, and approvals, and we produce artifacts for their sign-off.
What if we already have an AI pilot?
We can harden it with governance, evals, and evidence packs, or move it to production safely. See the Governance & Evidence Pack.
Limited availability
Request an AI Integration call
Founder-led, limited slots. We prioritize service businesses and lean teams that need practical AI adoption, workflow modernization, and measurable operating outcomes.
- Cloud-agnostic with AWS depth; recommendations stay grounded in your current stack.
- Evidence-first: evals, audit trails, governance checklists, and rollout plans.
- We respond within 2 business days with a short agenda.
Request an AI strategy call
Tell us the workflow and stakeholders. We review the fit first, then follow up with next steps and a scheduling link when the engagement makes sense.
Assessment-first scoping. Practical pilots, governance-aware delivery, and cloud operations depth where it matters.