Every Beacon product runs on the same platform: a purpose-built knowledge layer that connects to your existing systems, keeps your documents in your control, and makes every AI query traceable to its source and its cost.
State DOTs have spent decades building ProjectWise repositories, SharePoint document libraries, and public data feeds. Beacon doesn't replace any of that. We read from your existing systems, index the content, and make it answerable.
The document stays where it's always been. The SOP stays in OneDrive. The concept report stays in ProjectWise. Beacon adds a knowledge layer on top — one that keeps in sync as your documents change and answers questions from a single interface instead of five different logins.
The first question agencies ask us is whether this requires a rip-and-replace of their existing infrastructure. The answer is no. We have never asked a DOT to move their documents to get started.
Trust, Throughput, Tokens, and Transparency. Every product Beacon builds is held to all four.
Citations are the floor, not the ceiling. Every response links to its source passage — but trust is built through continuous evaluation. 520 golden Q&A pairs run weekly against every active deployment, scoring retrieval, faithfulness, and citation accuracy against defined thresholds. When something regresses, Beacon staff sees it before operators do.
State DOT document environments are large. One state DOT's ProjectWise environment alone contains 10.5 million documents. Beacon is built to index at that scale, across multiple data sources simultaneously, and stay current as documents change. When a file is edited, only the changed section is re-embedded — the rest of the index is untouched.
When Uber's CTO admitted he'd burned his entire 2026 AI budget in a few months, the underlying problem was invisible spend. Token spend scales with usage, and usage grows faster than most teams expect. For any organization with a budget owner to answer to, that's not a minor issue — it's a real one.
Beacon is built model-agnostic by design. The knowledge layer — your index, your citations, your evaluation history — lives independently of whichever model is doing the inference. Swap Anthropic for OpenAI or vice versa without rebuilding anything. That's not just a technical convenience; it's leverage. You can credibly take your business elsewhere, which keeps providers accountable to your terms.
State DOTs hold decades of irreplaceable operational knowledge — how to price a complex estimate, which responder to call on which corridor, what a pattern of incidents actually means on that stretch of road. Every API call to a frontier model without proper data controls is, at some level, a transfer of that institutional knowledge to a third party whose incentives don't align with yours.
AI model providers have a structural incentive to absorb as much intelligence from their customers as possible — it sharpens their models, which they then lease to your competitors. The question agencies should be asking isn't just "is our data private?" It's "who owns the intelligence our operational data contains, and who does it compound for?"
Beacon's architecture is designed so the answer is always: you. The retrieval index lives within a perimeter your IT team controls. The model sees only the retrieved passage. Your operational knowledge stays yours — and compounds for your agency, not for a model provider.
Beacon runs a structured evaluation suite on every deployment, every week. 520 golden Q&A pairs — questions with known correct answers — run through the full pipeline and score against six RAG metrics. When something regresses, we know before you do.
The evaluation layer covers the failure modes that actually matter in government AI: hallucinated facts, broken citations, stale sources, and answers that are technically retrieved but not actually relevant. Each metric has a threshold. Drops below the threshold trigger immediate review.
Operators see a summarized trust score. Beacon staff sees the full metric breakdown. When we find a regression, we fix it and tell you what changed — not at the next quarterly review, but within the week.
Government AI procurement requires vendors to be honest about where they actually stand on compliance — not to oversell certifications they haven't earned. Here's where Beacon actually stands.
We've done the architecture work that makes compliance achievable: threat modeling, NIST SP 800-53 family mapping, a four-role authorization model with explicit no-escalation invariants, and an audit log with hash-chain verification. The foundation is real. The certifications take time.
For agencies with immediate procurement requirements, the path we've used is a public-data-only pilot that requires no DIT or state security review to start — giving agencies six months to work the compliance path in parallel while already seeing value.
Architecture mapped against NIST SP 800-53 control families. 24 threat models documented with attacker profiles, mitigations, and residual gaps.
Every operator interaction logged with tamper-evident hash chain verification and configurable retention to match agency records policies.
In progress for NCDOT engagement. GCP Assured Workloads architecture scoped. Working through state-specific security review processes.
On the roadmap. Not something we'll claim until the audits are done. If your engagement requires it, let's talk timeline — the architecture is built to support it.
Every query your staff runs generates signal: what was asked, what was retrieved, what was useful. That signal flows back into the knowledge layer — improving retrieval, surfacing gaps in your documentation, and sharpening the system for the next query. Usage → signal → better knowledge → better answers → more usage. That's the flywheel.
That flywheel only compounds value for the organization that owns the index. Run it through an AI model provider's closed system, and the intelligence accrues to them. Run it through Beacon — where your agency owns the index — and it accrues to you. Each product deepens the same substrate: TMC-React indexes your SOPs, Estimator adds historical project data, Pulse brings in 27 public data feeds. Each one makes the next more powerful.
Via a native MCP server, that compounding knowledge is accessible from Microsoft Copilot, Claude, or any AI tool your agency already uses — without custom integration work for each one. The knowledge travels with you — not with the model provider.