Software that helps students learn faster, and never invents a source
Describe what you want to build. You will be asked about the data behind it, what has to be true of the information, and what the business needs, then handed a requirements brief. You can see it think.
A student learning accelerator for Christian colleges and seminaries. Ready at 354 Populi powered campuses, backed by a research library of 611,789 indexed passages, with a Socratic coach that asks the questions and never writes the paper.
Built for NYU students. The Apostles engine re-tuned for the NYU core curriculum: help that already knows the semester course by course, adaptive practice drawn from the course's own materials, and a writing coach that sharpens the argument instead of ghostwriting it.
Agents that execute real operational procedures across several lines of business, under human command and reporting to one dashboard. Built fail closed: when anything is uncertain it stops and asks, and every action is accounted for.
Decades of expert reviews, ratings, and tasting lore gathered in one place, then made answerable. Straight teardowns of what a cigar delivers against what it costs, built for collectors, lounges, and the trade.
Apostles does not reason from memory. It reasons from a theological research library indexed passage by passage, so a student can follow any claim back to the page it came from. The catalogue keeps growing while enrichment continues.
A Socratic writing coach that interrogates the argument, surfaces the weak link, and points at the source the student has not read yet. It will not hand over prose, because the point is the student learning to make the case.
Realtime Data Solutions is the practice of Travis Dayton, a data architect who ships production systems in regulated, high stakes domains, from patented financial controls infrastructure to document intelligence pipelines. Values we build by: integrity, stewardship, dignity.
Travis Dayton
Data architect and AI engineer. Based in Barcelona.
Travis Dayton has spent more than fifteen years building and governing data systems inside regulated industries, and now builds agentic AI that can be trusted inside those same environments.
His career has been shaped by sectors where a wrong answer carries consequence. At Bayer he established enterprise data governance across pharmaceutical research, regulatory affairs and commercial domains under 21 CFR Part 11 and global GxP. At Gulfstream Aerospace he delivered under ITAR and DFARS, where the nationality and location of the person touching the data is itself a legal control. Before that he authored data governance policy at Portland General Electric under NERC and FERC, and at Andor Health he built governed pipelines for a platform carrying clinical data under HIPAA. GDPR runs across all of it.
That background is why the AI here looks different. These systems cite their sources, refuse rather than invent, and stop to ask a person when the ground is uncertain. They ship as LangGraph applications through one deployment pipeline, with production inference running on owned hardware, so client data does not have to leave the perimeter.
Requirements here are not a document that goes stale beside the code. They are the program. The specification is itself a LangGraph: every requirement is a node, decomposed until a leaf names exactly one function that has to exist, and agents build against that graph rather than against a brief.
That is what a LangGraph specification is. The requirements, the order they run in, and the test that proves each one are a single executable graph, so the specification can be run, and can fail, in the same way the software can.
A requirement stays a parent until it can name exactly one implementable function. The tree keeps going down until there is nothing left to interpret.
A second relation records what must have run first at runtime. The tree says what the thing is made of. The graph says what happens when. They are deliberately not the same relation.
Each leaf resolves through an implementation registry to a callable function. Correspondence is a property of the code, not a claim in a table, so the specification cannot quietly drift away from the build.
Every leaf carries assertions that call the function and check the behaviour the requirement asks for. A leaf with no assertion fails. You cannot add a requirement without saying how you would know it works.
Once the LangGraph specification is machine readable and gated, the build is handed to agents. They have somewhere exact to aim, and the gate is what tells them, and me, the moment they are wrong.
Every system below solves a named problem for a named kind of user. What is listed under each is the architectural decision that makes it work where a general purpose assistant would not.
A study companion for Christian higher education, pulling coursework, deadlines, email and calendar into one agentic interface.
The architectureCanon access is isolated per confession and fails closed, so a college only ever reaches the corpus its own tradition entitles it to. The coach is Socratic by construction and cannot write a student's prose for them.
Open it →The same engine aimed at a large secular university, planning a student's week around the deadlines they actually have.
The architecturePriority and urgency are reasoned by a model over the student's own graph, never by hardcoded date arithmetic, so the plan bends to the person instead of the calendar.
Open it →The question box at the top of this page. Describe what you want to build and leave with a requirements brief.
The architectureA ReAct interview whose full reasoning trace is a hard requirement rather than a debug view. It records what you stated separately from what it inferred, and hands you to a person rather than inventing an answer.
An operator owner running several lines of business, where deposits have to be reconciled and posted against a ledger every single day.
The architectureEvery node that can move money sits behind gates that fail closed, and the robot driving the vendor console is grounded in a stored atlas of that interface rather than guessing at the screen in front of it.
A group that has to show an auditor why its AI answered the way it did.
The architectureOne container carries the graph, the warehouse and the evidence trail together, so any answer can be walked back to the record it came from without leaving the perimeter.
Turn a market segment and its public reviews into a complete, sellable engagement package.
The architectureA LangGraph that manufactures other LangGraphs. Quality is enforced by gates rather than by a reviewer, and a run that cannot pass escalates with the exact failure instead of shipping something plausible.
Turn the recording of a discovery call into engineering knowledge a team can build from.
The architectureThe pipeline reconstructs the information value chain out of speech, emits a structured dossier, then leaves the requirements themselves conversational so you can interrogate them afterwards.
Catch the grounded looking answer that the retrieved sources do not actually support.
The architectureA scorer held resident on the card and shared by every application on the box. It is an independent second opinion, and by design it can only ever make a decision stricter, never looser.
Turn a walkthrough video of a company's floor into a priced engagement quote.
The architectureA local vision model describes what is genuinely happening in the frames, and every line of the quote must cite the observed activity it rests on, so a number cannot be invented.
Patient intake for clinics: booking, messaging in the patient's language, reminders and billing questions.
The architectureBuilt once and configured per clinic. Each node answers a specific complaint quoted from real reviews, so the architecture diagram doubles as the demonstration script.
A private investor buying individual tenanted residential stock in Barcelona.
The architectureOne physical property is resolved across several portals that never quite agree, then joined to the public administrative record, so an asking price can be tested against what is actually registered.
Self employed workers, immigrants and their accountants trying to know what the law actually allows.
The architectureA bitemporal, citation aware corpus of the law sits beside a catalogue of legal levers, and the euro impact of each one is computed arithmetically rather than described by a model.
Price a probabilistic edge against a sportsbook and stake it under hard risk limits.
The architectureA closed build, measure and learn loop. The system scores its own calibration and its closing line value, then recalibrates and promotes its own models on that evidence rather than on a hunch.
Let an assistant remember work done in sessions that have already closed.
The architectureEvery turn is chunked, embedded and written into a tenant of one lakehouse, so reasoning from months ago stays semantically searchable from any later session.
Transcribe a call while you are still in it, then summarise it and file it.
The architectureThe recorder only ever adds read streams to the audio server. It never creates a sink, moves a stream or changes a default, so capturing a call cannot disturb the call it is capturing.
Explain, on stage and on camera, why an agent can be trusted with real work.
The architectureA live illustration of the single idea beneath every build here: the model is a stochastic function, and the graph wrapped around it is the deterministic harness that makes its output checkable.
A telehealth product covering the patient front door, the clinician surface, and the journey between them.
The architectureThe care journey is modelled as an explicit stepper from pre visit through to follow up, so every surface knows where the person actually is in their care instead of treating each screen as a separate application.
Publish authority content that diagnoses why a business is actually failing, rather than what its marketing claims.
The architectureIt mines the whole of a company's public reviews and website, then produces a grounded architectural teardown. Identity is stripped by construction, and the report is never sent to the company it examines.
Turn any large structured text corpus into a knowledge base that can genuinely be reasoned over.
The architectureEmbeddings at several resolutions sit beside a concept graph with hybrid retrieval and reranking. It only counts as working once it ingests a second, unrelated corpus with zero code changes.
Land business conversations in the warehouse without touching the account they came from.
The architectureAn extension reading an already open session, because vendor device linking now demands a passkey no headless server can produce. It never writes, and a gate refuses to start the service at all if it could.
Get every published artifact of a continental governance index into one queryable place.
The architectureA graph orchestrated harvester rather than a download script, because the portal scatters one dataset across workbooks, bundles, a codebook in four languages, reports and fifty four country profiles.
Build the full data inventory that matching, investor packs and comparables are later assembled from.
The architectureA content addressed raw zone under a hard storage ceiling, so the same listing fetched twice costs nothing and a long crawl cannot quietly eat the disk.
Give an agent a link and let it return named fields, or an entire site, as structured data.
The architectureThe HTML parser, the selector engine, the robots parser and the crawler are all written inside the package, so a tool an agent depends on carries no runtime dependency nobody has read.
Run a third party client entirely against a model on hardware you own.
The architectureTwo containers and one boundary. Every call the client makes terminates in a local gateway, and the credentials that shipped inside the vendor's own application are never used, stored or referenced anywhere.
A premium ritual product whose real asset is the thinking companion behind it.
The architectureThe companion is exposed as a connector inside the assistants people already use, backed by workflows and a reflective graph, so it lives where the reflection actually happens rather than in one more app.
A speaking presence for live demonstration and recorded video.
The architectureThe face is a rendering surface on a virtual camera and the brain is a headless coding agent, so the thing on screen answers with the same reasoning that ships the work.
Produce investigation styled visual sequences for brand and campaign work.
The architectureEvery technique in the board traces to a researched claim that survived verification, and the ones that were refuted were dropped, so a look can be defended and repeated instead of art directed from nothing each time.
Most of this work sits under client confidentiality, so each system is described by what it does and how it is built rather than by who it was built for. Where something is public, there is a link.
Different worlds, from seminaries to operations floors, but every product is held to the same three house rules.
Every claim is traced to a real source the reader can open. Answers come from indexed material, never from a model's recollection, and a citation is part of the answer rather than a footnote bolted on afterwards.
When the sources do not settle a question, the honest answer is that they do not. Every system here stops and hands control back to a person rather than guessing, because a confident wrong answer costs more than a pause.
A student's work belongs to the student and a client's data belongs to the client. Formation and operations both stay behind the wall they were built in, and nothing leaks sideways into a leaderboard or a training set.
If your institution, your operation, or your obsession deserves software this grounded, write. Tell us the world it lives in and you will get back something real: what it would take, what it would cost, and what it would do on day one.