The art of the possible.
How we unlock enterprise AI in the real world—with structure, not speculation.
We built Likeable because we were tired of fixing AI projects from the inside.
Before this company existed, we designed and deployed high-risk AI systems inside enterprise environments—healthcare networks, financial institutions, retail chains, and logistics providers. We saw the mistakes up close. We saw what worked. And we saw what got buried under politics, vague pilots, or tech that didn’t scale. Likeable exists to do it right the first time. What you see here isn’t conceptual. It’s drawn from what we’ve actually built—now packaged with the clarity and frameworks we wished we had back then.
Orchestrating HIPAA-safe GenAI across 30+ locations
We designed a system where clinician voice notes, triage text, and EMR records were ingested through an air-gapped local LLM cluster. Every prompt and every inference was logged against a HIPAA-ready audit layer. Data never left the client’s network. Results were routed through a permissions-controlled summary engine for review before being made available downstream.
We first built this architecture inside a national provider network struggling with burnout, fragmented patient data, and compliance reviews that stalled innovation. That system is the blueprint we now deliver through Likeable—with less overhead, stronger controls, and a faster runway.
Structuring in-store behavior with multi-modal data orchestration
At a global retail chain, we helped integrate store cameras, voice sentiment, and POS data into a single intelligence stream. The orchestration layer filtered and structured visual feeds, aligned them with product-level telemetry, and triggered layout adjustments based on real-world engagement and conversion. Most vendors sell AI vision with no context. We built this system so operators could adjust promotions in real time, based on conditions they could explain to their merchandising, marketing, and compliance teams. That orchestration model now lives inside our platform.
Deploying a policy agent that understands regulatory drift
We helped a financial client automate review cycles for regulatory changes by building a secure agent that monitored updates from FINRA, OCC, and SEC sources. The agent parsed new text, aligned it to internal controls, and created explainable diffs for risk and compliance leads.
Every inference was logged with source traceability and reviewer confirmation—fully auditable. Likeable didn’t invent this use case. We perfected it. The orchestration system we use now is built on the scaffolding we first deployed under tight legal pressure, short timelines, and zero margin for interpretation.
Predicting failure before it costs you routes, customers, or revenue
We helped a transportation firm combine asset telemetry, vehicle diagnostics, route data, and external signals like weather and local traffic into a predictive risk engine. The platform triggered maintenance scheduling, rerouting, and exception management automatically, and delivered summaries to fleet managers and regional planners in real time.
The value wasn’t the model. It was in how we connected inputs, logged system behavior, and kept IT, ops, and legal aligned the entire way. That full-stack orchestration pattern is now embedded in our deployment frameworks.
Turning buried IP into active intelligence
We helped a life sciences team build a private knowledge engine that ingested 10+ years of internal trial data, FDA feedback, investigator commentary, and compliance documentation. The system didn’t just summarize—it contextualized findings by study, molecule class, and regulatory body. We didn’t use open APIs.
We built the pipeline using Likeable’s ingestion framework, tagged every document, validated output by confidence and origin, and routed summaries back to R&D leaders. This wasn’t just a knowledge base. It was a strategic asset you could defend.
What makes us different.
Other firms mention these use cases. We’ve built them. Then we built the frameworks to do it again—faster, clearer, and without the chaos that kills most enterprise AI work.
These aren’t hypothetical scenarios. They’re models we’ve seen succeed. The difference now is that we bring full orchestration, auditability, and executive visibility to the table from the start.
What’s possible inside your business?
Let's map it. If your team is evaluating or deploying a high-commitment AI initiative—across infrastructure, LLMs, multimedia, or compliance-first orchestration—this is where we start.