Delivering enterprise AI-powered solutions at scale.
HALO designs, builds, and operates production AI for the enterprise. We build the intelligent systems, agent workflows, and governance frameworks that take your AI initiative from a stalled experiment into a compounding operational advantage.
Roadmaps are full of proofs-of-concept that never see a user. Between a demo and the deployed system sits integration, governance, data readiness, adoption, and more. That gap is where most 'experimenters' AI budgets disappear, closing it is the entire premise of HALO's Applied AI practice.
Very few are turning those experiments into secure, scalable systems.
Most teams don't fail at AI because of the technology. They fail because of uncertainty: unclear value, brittle prototypes, and siloed experiments.
AI challenges we solve.
Moving beyond the prototype
A large share of our engagements begin with a prototype someone else built. We assess what's salvageable, rebuild what isn't, and take it through governed deployment on a timeline measured in weeks rather than quarters.
Aligning AI with Business Objectives
We define success metrics before development starts and tie them to numbers your executives already track. The system is instrumented so impact can be measured after launch rather than asserted.
Misunderstanding what GenAI can and can't do
Generic tools hit their ceiling fast, and knowing where that ceiling sits is what makes scoping useful. We build domain-specific systems that act on your data inside your workflows: underwriting assistants, clinical copilots, inventory analysts, compliance reviewers.
Data fragmentation & model fit
Most enterprise data arrives fragmented, and preparation is part of the engagement rather than a prerequisite for starting one. Discovery, schema mapping, and readiness assessment make up the first phase of the build.
Security & private development
We architect for private deployment and in-perimeter inference, choosing models and hosting that keep sensitive data inside your boundaries, with per-user and per-agent access limits enforced at the data layer.
Low adoption & poor ux
Low adoption traces back to design, and no amount of training fixes it after the fact. We build the interface alongside the model rather than after it, and instrument usage from day one so drop-off points surface early enough to correct.
Our Differentiators
The difference between an AI vendor and your product & engineering partner.
We've spent 20 years building the systems enterprises actually run on: regulated finance platforms, global commerce infrastructure, healthcare operations. In 2022 we formalized that depth into our AI-Forward Innovation program, and the practice has since delivered dozens of production AI initiatives.
35+ Production systems and counting
Semantic search running inside a high-volume commerce operation. Agentic underwriting on a fintech platform. Copilots wired into enterprise data systems. Six years of shipped AI work, in production, with names attached.
Forward-deployed, end-to-end
The strategist who scopes your roadmap and the engineer who ships it sit on the same team, often in the same meeting. Consulting rigor up front, forward-deployed engineers through launch and beyond, with nothing lost to a handoff because there isn't one.
Senior people who stay on the work
The team that scopes your engagement is the team that builds it. Senior AI engineers, solution architects, and designers carry the work from kickoff through production, so context lives with the people writing the code instead of in a handoff document.
Compliance is a design input
Entitlement, auditability, and explainability get decided in the first architecture diagram, never retrofitted after a security review. We carry ISO 27001 certification and have shipped AI under PCI-DSS, HIPAA, and PHI handling requirements.
The capabilitiy set behind every system we ship.
Each capability below runs as its own discipline and connects to all the others, so an engagement can start anywhere: a strategy question, a stalled prototype, or a live system that needs to scale.
