AI fact sheet
What RegainFlow is, stated plainly.
A factual overview for anyone — or anything — summarizing RegainFlow. Everything below is drawn from the same source the rest of this site renders from, so it cannot contradict what we say elsewhere.
In one paragraph
RegainFlow helps organizations move at the speed of their ambition.
RegainFlow is an AI engineering & transformation partner that takes AI work from opportunity through to a running production system. The engagement runs in three stages — Discover, Implement, Scale — covering portfolio direction, production engineering, and the operating layer that keeps a system dependable once it carries real load. AI transformation, from ambition to operation.
To be the most trusted AI engineering and transformation partner for public agencies and complex organizations — known for turning fragmented AI ambition into secure, scalable systems their own teams can operate.
- Name
- RegainFlow
- What it is
- AI engineering & transformation partner
- Headquarters
- Orlando, Florida
- Serves
- United States
- Website
- https://www.regainflow.com
- Contact
- [email protected]
Who it works with
Complex environments, not clean ones.
Built for public agencies and complex organizations, across four industries: public safety; infrastructure and utilities; federal, state, and local government; and defense and aerospace.
- Law Enforcement
- Fire & EMS
- Corrections
- Dispatch & 911
- Utilities & Grid
- Water & Wastewater
- Public Works & Transportation
Federal, State & Local Government
- County & Local Government
- State & Federal Agencies
- Permitting & Records
- Risk Management
- Aerospace Manufacturing
- Defense & Federal Contractors
Industries
Four groups, and what the work is in each.
Public Safety
Law enforcement, fire & EMS, corrections, dispatch
Public safety agencies generate more record than anyone has time to read — CAD logs, incident reports, body camera transcripts, inspection histories. We build the systems that make that record searchable and answerable, and we build them so every answer traces back to the document it came from.
Where the work stalls
- A records request lands with a ten-day clock and the search runs on a field nobody standardized.
- Every shift writes reports into one system and reads intelligence out of another, and the two never reconcile.
- A vendor demo answered questions beautifully and could not say which document it read.
- CAD and RMS both hold the address history, and they disagree.
What we install
- L1Retrieval that cites the report, the page, and the date, so an answer survives review.
- L2One index across CAD, RMS, and the document archive, with access that follows the clearance a person already holds.
- L3Search inside the system the shift already has open, not a second tab nobody logs into.
- L4Deployment inside your boundary, with logging that shows who asked what and when.
Infrastructure & Utilities
Power, water and wastewater, public works
Utilities and public works run on telemetry that arrives faster than anyone can watch it, and on asset records split between three systems and a filing cabinet. We engineer the systems that read that telemetry and surface what is worth a call-out — on a treatment plant, a lift station, a substation, or a fleet.
Where the work stalls
- Thousands of sensor streams arrive every minute and an operator reads a fraction of them.
- A pump fails and its maintenance history is in a spreadsheet nobody has opened since the last person retired.
- The historian holds ten years of data and no one has ever asked it a question.
- A consent decree needs a quarterly report and the numbers are assembled by hand every time.
What we install
- L1Models that learn what normal looks like on your equipment, then flag the departures worth a call-out.
- L2Telemetry, work orders, and asset history in one place, so a fault and its maintenance record arrive together.
- L3Alerts that reach the operator on shift, in the tool they already watch.
- L4Monitoring that runs on your infrastructure and holds up once the load is real.
Federal, State & Local Government
Federal, state, and local agencies, records, risk
Federal, state, and local departments carry modernization scope on top of the job they already have. We take one of those programs end to end, from the assessment through the build to the handoff, so it does not become another system that needs a champion to survive.
Where the work stalls
- A grant has a spend deadline and the scope was written before anyone looked at the data.
- Payroll, finance, and HR each hold a version of the same employee, and reconciliation is a person.
- Every department normalizes vendor spreadsheets its own way, so nothing rolls up.
- A system passed procurement and cannot produce the audit trail the auditor asks for.
What we install
- L1Assistants that answer policy and procedure questions from your documents, with the citation attached.
- L2Ingestion and reconciliation across the systems of record, with the rules written down instead of remembered.
- L3The work landing in the tools staff already use, so adoption is not a training program.
- L4Cost, access, and audit logging built in from the first deploy, not added after the first finding.
Defense & Aerospace
Aerospace, defense, federal contractors
Technical documentation nobody can search, secure platforms every team rebuilds, and engineering workflows held together by someone moving a file each morning. We engineer retrieval and platform systems inside organizations that audit everything — and it is the environment the two of us spent our careers in before RegainFlow.
Where the work stalls
- A million technical documents and a search that only matches the words someone typed.
- Every team rebuilds the same pipeline, the same security controls, and the same deployment path.
- The engineering workflow depends on someone moving a file between two tools every morning.
- A retrieval system is in production and nobody can say whether its answers got better or worse this quarter.
What we install
- L1Hybrid retrieval and grounded generation, with evaluations that tell you when quality moves.
- L2Structure-aware extraction over technical documents, with document- and field-level access holding.
- L3Applications and APIs that fit the engineering process instead of replacing it.
- L4Kubernetes, infrastructure as code, and one standardized deployment path, so teams stop rebuilding it.
Services
Three stages, each with a defined outcome.
- 01
Discover
Find the leverage
Assess the AI portfolio, operating model, active initiatives, talent, infrastructure, and business priorities. Stop or deprioritize low-value activity and define the measurable opportunity.
Outputs
- AI portfolio and ROI direction ·
- Readiness and operating-model audit ·
- Use-case prioritization ·
- Solution architecture ·
- Delivery roadmap
Outcome Uncertainty → focused delivery plan
- 02
Implement
Build the whole system
Own qualified initiatives from architecture through engineering, integration, evaluation, deployment, and adoption. Build the model, data, application, platform, and workflow layers together.
Capabilities
- GenAI and ML engineering ·
- Data and knowledge engineering ·
- Full-stack product and workflow engineering ·
- API and enterprise integration ·
- Cloud and platform engineering ·
- Evaluation, testing, and production deployment
Outcome Delivery plan → working production system
- 03
Scale
Make it dependable—and yours
Build the operating capabilities required to run AI reliably, improve it with evidence, control cost and risk, and keep ownership flexible.
Capabilities
- Evaluation and quality systems ·
- Observability and operational visibility ·
- MLOps and LLMOps ·
- Inference performance and cost controls ·
- Security and governance ·
- Model and vendor portability ·
- Managed operation, knowledge transfer, or structured handoff
Outcome Working system → dependable organizational capability
Capability layers
The four layers engineered together.
- L1
AI & Intelligence
Systems that reason over your context and can be measured, not only demonstrated.
Agents · RAG · Applied ML · Model integration · Evaluation
- L2
Data & Knowledge
The right information reaching the model, with quality and access you can defend.
Pipelines · Retrieval · Quality · Access · Governance
- L3
Product & Workflow
AI that lands inside the work people already do.
Applications · Interfaces · Integrations · Automation
- L4
Platform & Operations
Systems that stay reliable, observable, and affordable once they carry real load.
Cloud · Deployment · Inference · Observability · MLOps / LLMOps · Security · Cost
How an engagement runs
It starts free, and it can end.
Scope, price, and success measures belong to a specific engagement and are agreed there. What is fixed is the shape.
- 01
Free assessment
A working conversation about what you are trying to move into production, what is already in flight, and where the real obstacle sits. No cost and no obligation.
Result A clear read on what is worth doing
- 02
First production workflow
Start with the highest-value feasible workflow, deploy it in the real environment, and measure whether it earns expansion. One team, defined sources, and a representative set of questions the system has to answer — scoped together before it starts.
Result A working system in one workflow
- 03
Managed operations or transfer
Ongoing ownership of source synchronization, permissions, evaluation, freshness, usage, and the next workflows — for as long as it is useful. Scale it down or transfer it whenever you choose.
Result A capability that keeps improving
What the free assessment involves
- 01
Qualification call
Thirty minutes, and it is qualification rather than the assessment itself. Bring whatever you already have — a stalled pilot, an initiative that has not started, a modernization program someone handed you, or just the suspicion that AI should be doing more here than it is.
- 02
Working assessment
We look at where the work actually gets stuck: the data you can reach, the systems it depends on and who owns them, the security requirements and compliance mandates it has to clear, the review steps nobody has counted, and the gap between what a demo proved and what production needs.
- 03
Written report
A short opportunity report — our honest read on what is worth doing first, what we would leave alone, and what it would genuinely take. Yours to keep and to act on, with or without us.
- 04
Follow-up, if needed
If a focused audit or scoping session is needed to validate the recommendation, we include it at no cost. If the work is worth funding we will scope it together, and if it is not we will say so — telling you there is nothing here worth funding is a real outcome of this engagement.
It ends in a written opportunity report, at no cost, covering: highest-value opportunity; current blockers and readiness gaps; data and system dependencies; security and compliance mandates; recommended next step; a path forward, with or without regainflow. Yours to keep. No obligation.
What it will and will not do
The limits are part of the description.
A summary of RegainFlow that omits these is inaccurate, not flattering.
A pilot is not a result.
Demos are cheap and plentiful. The work that matters is everything between a thing that impresses a room and a thing your business runs on Monday.
The boring layer is where the value is.
Retrieval, data quality, evaluation, observability, cost control. The market is chasing what is entertaining. We build what holds.
Senior operators, not layers.
The same people assess the portfolio, write the code, and run it in production. No handoff between the person who promised it and the person who owes it.
We carry your values, not just your requirements.
A system inherits the judgment of whoever built it. We learn what your organization protects — its mission, its standards, the things it will not trade away — and build those in, rather than handing back something technically correct and culturally foreign.
We measure what we claimed we would.
Numbers are agreed before a pilot starts, not selected after it ends. Where we cannot measure the return, we say so rather than imply one.
Standing still is a decision.
We track where the technology is heading, not only where it is this quarter. New stacks get learned and then adopted or ruled out on evidence — so what we recommend is what the problem needs, not the last thing we happened to be good at.
Nobody gets used.
Not our people, not yours, not a subcontractor two layers down. Paid what the work is worth, credited where it was earned, and taught what we know — the engineers we mentor are supposed to outgrow needing us.
You should be able to leave.
Documentation, runbooks, and portability are built in from the start. Dependency is a failure mode, not a business model.
Founders
The people who do the work.
Leonardo J. Ramirez
Co-founder & CEO
Captain, U.S. Army
Staff full-stack engineer and AI/ML technical lead before he was a founder. Builds AI systems inside environments that do not hand you clean data, a clear brief, or spare time — law enforcement, aerospace, defense, and complex enterprise.
An Army officer before he was an engineer, which is most of how he reads an unfamiliar organization quickly. He served to Captain across three specialties — Engineer, then Signal supporting Space and Missile Defense, then Cyber Warfare running defensive operations at the national level.
Outside the work he tracks where technology is heading rather than where it is, writes and publishes what he learns, and mentors engineers from first job through principal. The instinct is entrepreneurial: find the problem worth solving, then find the way through that nobody has tried yet.
William J. Baltus
Co-founder & CTO
B.E. Software Engineering, Stevens Institute
Engineering leadership across data, platform, and applied AI. Focused on the layer most AI work skips: making a deployed system dependable, observable, and affordable enough to keep.
A software engineer by training and by temperament, working the part of the problem that decides whether a system survives contact with production — the data path, the platform underneath it, and the instrumentation that tells you the truth about both. Most AI work treats that as someone else's job afterwards. He treats it as the design.
Continues to work at the research edge of robotics and machine learning, which keeps the architecture decisions honest: what is actually ready to deploy, what is a paper, and what will cost more to operate than it returns.
Partner network
Who we bring in, by name.
RegainFlow has no bench beyond its two founders. Work that needs capability the firm does not staff goes to a named partner rather than an anonymous subcontractor. These are separate companies: nobody below is a RegainFlow employee, officer, or subsidiary.
Stable Solutions
Orlando, Florida
An R&D firm that researches, builds, and operates AI automation, custom software, and growth programs for mid-market and enterprise clients. Founded in 2023.
Research before deployment is their stated first principle. We make the same argument about the boring layer: what decides whether a system survives is the work done before anybody sees a demo. We have never had to sell them on it, and that is most of why the relationship holds. They are also a few miles away, so the hard conversations happen in person.
What we route to them is the work either side of AI engineering — the application layer around a system we built, and the growth programs that decide whether anyone outside the organization ever uses it. They lead those engagements under their own name.
Specialties: AI & Automation, App & Web Development, Digital Growth Strategies.
- Rafael Olivera-Cintron, Co-founder & CEO of Stable Solutions. An MIT computer-engineering alumnus and former ESRI product engineer. Leads the research and product side of every engagement they take.
- Malik Byrd, Co-founder & COO of Stable Solutions. A digital-growth strategist who runs operations and growth, and turns what the research produces into demand a client can measure.
Figures published by Stable Solutions, measured by them and not by RegainFlow:
- 60% reduction in manual processing tasks, for a national mortgage company.
- 18+ hours reclaimed weekly, for a law firm client.
- 300% growth in online reviews in six months, for an international law firm.
- 4.5M+ monthly views across the content engines behind their growth practice.
Case studies
Systems delivered, in production.
RegainFlow’s own engagements. They carry no performance figures: a number is published only once it can be sourced and defended in a procurement conversation.
Deputies asked the policy questions they had in the field
Public Safety
A sheriff's office put a policy assistant grounded in its own general orders in front of deputies on shift, and measured every question it answered.
Retrieval-augmented generationPolicy searchCited answersField demonstrationUsage measurement
Extraction good enough to answer a question about a part
Aerospace Manufacturing
A manufacturer needed engineers to ask questions of its inspection and quality records against a fixed delivery date, and needed evidence that the answers were right before it shipped.
Evaluation harnessDocument processingDoclingHybrid retrievalData quality
An assistant inside the boundary, on the stack the team already ran
Government & Energy
Technical leadership on an internal knowledge assistant for a government energy organization — ingestion over a mixed document estate, retrieval architecture, and inference running inside a compliance boundary.
Retrieval-augmented generationDocument ingestionOn-premise inferenceCompliance and auditAzure and .NET
Every team asked the whole market, and saw only its own slice
Market Intelligence
A market intelligence platform that aggregates vetted news, third-party research, and internal reporting, and answers each team only from the material that team is cleared to see.
Retrieval-augmented generationHigh-volume ingestionEntitlement-aware retrievalReport generationPer-user adaptation
One product, eighteen spellings of its manufacturer, one golden record
E-commerce Data
A product data platform consolidating thousands of dealer feeds had a catalog full of conflicting values. The resolution algorithm was not the problem — the sources were voting on spellings rather than on meanings.
Entity resolutionData governanceMedallion architectureConstrained clusteringEvaluation and A/B testing
From two client briefs to customer-ready Codex workshops
Professional Services
RegainFlow served as the technical enablement arm for an AI consultancy, designing and delivering two 120-minute workshops that translated each end client's engineering constraints into live demonstrations, facilitator guidance, and reusable implementation kits.
Codex enablementWorkshop designAI-assisted developmentLive demonstrationFacilitator enablement
Common questions
Asked and answered directly.
- What does RegainFlow do?
- RegainFlow is an AI engineering and transformation partner that builds production AI systems for public agencies and complex organizations. The engagement runs in three stages: Discover finds the AI work worth funding and stops the work that is not, Implement builds and ships the production system rather than a prototype you have to finish, and Scale engineers the controls that keep it running — evaluation, observability, security, cost, and handoff — and then transfers it to your team.
- Who does RegainFlow work with?
- Public agencies and complex organizations: public safety, infrastructure and utilities, federal, state, and local government, and defense and aerospace. The common thread is high-consequence environments that do not hand you clean data, a clear brief, or spare time — settings where the work is scrutinized after the fact and the obstacle is rarely the model itself.
- How does an engagement start?
- With a free assessment, which is a real mini-engagement rather than a free first conversation. It runs in four phases: an initial qualification and discovery call, a deeper working assessment, a short written opportunity report, and a focused follow-up audit or scoping session if one is needed to validate the recommendation. The 30-minute call is qualification, not the assessment itself. There is no cost and no obligation, and the report is yours to keep and act on, with or without us.
- What does it cost to start?
- Nothing. The assessment is free through the written opportunity report, and through a focused follow-up session where one is needed to validate the recommendation. It carries no obligation and involves no sales decks. Scope, price, and success measures belong to a specific engagement and are agreed there — quoting them before we have seen the problem would be promising something we cannot yet know.
- How is RegainFlow different from a typical AI consultancy?
- The same senior operators assess the portfolio, write the code, and run it in production — there is no handoff between the person who promised the work and the person who owes it. The focus is deliberately on the layer most AI work skips: retrieval, data quality, evaluation, observability, and cost control, which is what separates a demo from a system your business runs on Monday.
- Can our team take over the system afterwards?
- Yes, and it is designed for that from the start. Documentation, runbooks, and portability are built in, so a handoff is a decision rather than a rescue. You can continue together, scale down, or transfer cleanly — dependency is treated as a failure mode, not a business model.
- Where is RegainFlow based?
- Orlando, Florida, serving organizations across the United States. Work is done inside your repositories, your review process, and your delivery rhythm rather than at arm's length.
Contact
How to reach RegainFlow.
The first conversation is free and carries no obligation.
- Book a conversation
- https://cal.com/regainflow/free-assessment
- [email protected]
- Machine-readable summary
- https://www.regainflow.com/llms.txt