Evaluate for real delivery.
See how people and AI agents work on an actual system: clear decision points, attributable work, quality checks built into delivery.
Enterprise-grade AI adoption
Already running AI in production? Put beacon platform on your shortlist.
Once your enterprise runs AI in its core delivery processes, the right AI engineering platform is the decisive factor. You need a way to keep responsibility clear, decisions traceable, engineering practices repeatable, and above all to actively shape Continuous Learning & Continuous Improvement.
Request a guided evaluationMaturity Assessment
Where are you on the AI-adoption curve?
Eight questions, two minutes. See your maturity level and biggest gaps, computed live.
Rate your maturityAI adoption maturity
From maturity Level 2 onward, include beacon. in your enterprise platform evaluation.
Once AI has proven itself in your organization, an AI-native platform starts to pay off. Anchoring AI in your core processes calls for a clear evaluation. The AI Adoption Maturity Model from Carnegie Mellon University's Software Engineering Institute and Accenture maps that journey. Our positioning is straightforward:
Evaluate beacon platform as you move into implementation, with alignment, scaling and future readiness in view.
Procure with scaling in mind.
Weigh shared workflows, governance, infrastructure and model choice before per-team approaches harden across the organization.
Put beacon platform to the test on your own work.
Bring a real engineering challenge and your enterprise requirements. Evaluate method and platform together: control, traceability, quality, sovereignty.
The maturity model tells you when to evaluate. beacon platform shows you how: your infrastructure, your keys, on your terms, on-prem or Swiss-hosted. You decide.
Rate your maturity in 2 minutesDisclaimer: CMU SEI & Accenture, AI Adoption Maturity Model v1.0 (2026). Practice questions and procurement positioning are our own interpretation, not a CMU procurement requirement or endorsement. Platform adoption alone does not establish organizational maturity.
AI Governance
Governance doesn't belong in a policy document. It belongs in the platform.
AI governance is the topic of the moment. The usual answer is a policy paper. But a document enforces nothing. Governance only works once it takes effect at runtime. That's exactly where it resides in our system: in the platform, which verifies and enforces every AI policy in real time. Sovereignty, Traceability, Control and Built-in Quality are seamlessly integrated into the operating model.
Sovereignty
No lock-in: not to a cloud, not to a model. Your platform runs where you choose; your model is your own, on your keys.
Traceability
Who did what, on which Spec, with which model. Every unit of work traceable, live in the product.
Control
Human-in-the-loop by design: go, adjust, stop at every checkpoint. No black-box autopilot.
Built-in quality
Spec-first, red-first. Quality is built into the system, not bolted on in review.
Your platform. Your infrastructure. Your rules.
Run beacon platform on-prem or Swiss-hosted, as a single instance or an org-wide site license. In your perimeter, under your contract, with your keys.
Two deployment modes
On-prem inside your own perimeter, or Swiss-hosted. You choose where your data and your models run.
Your keys, your contract
BYOK throughout. Model calls run on your own provider contracts, never through us.
Governance built in
Sovereignty, traceability, control and built-in quality live in the operating model, not a policy PDF.
A negotiated license
On-prem, single-instance and org site licenses are agreed individually. No list price, a real conversation.
Continuous Improvement
Three sources of evidence. One shared improvement process.
beacon platform connects Workflow Frictions, Engineering Metrics and Production Monitoring. Together they show where work stalls, how much time and money it takes, and how the delivered systems hold up in operation.
These insights feed a continuous process: Observe → Improve → Measure → Adapt. Spot problems, make targeted changes, verify the effect and evolve based on the results.
Workflow Frictions
Every workflow run yields signals about how work is done. The platform captures friction: unclear specs, repeated corrections, blocked steps or avoidable effort. Recurring patterns give concrete starting points to improve workflows, engineering practices and guardrails.
Engineering Metrics
Systematically captured Lead Time and Cycle Time make the flow of work visible. Active cost tracking shows spend by model, workflow and task. These metrics are the basis for evaluating change: is work completed faster? Does the correction effort drop? Does a different model save cost at comparable quality?
Production Monitoring
Production monitoring brings insights from running systems back into engineering. A bug can trigger both a fix and an improvement to the check or the workflow that let it through. Subsequent releases show whether the addressed defect class recurs and how quality develops in operation.
Observe → Improve → Measure → Adapt
All three sources feed the same improvement process. Workflow Frictions help explain anomalies in the metrics. Engineering Metrics make effort and cost visible. Production Monitoring shows how the results hold up in the field.
Observe · Spot the pattern.
Look at friction, metrics and production signals together. Identify a concrete problem and record the baseline: what happens, how often it occurs, and what impact it has on time, cost or quality? That yields a testable improvement goal.
Improve · Make a targeted change.
Translate the observation into an improvement hypothesis and a concrete change. That might be an adjusted workflow, a more precise spec, an additional quality check or a different model choice. Record what is changed and what effect is expected.
Measure · Verify the effect.
Subsequent runs and releases provide the basis for comparison. Look at Lead Time, Cycle Time and cost before and after the change. Check whether the addressed friction or defect class recurs. Use comparable tasks and time frames, and evaluate quality, speed and cost together.
Adapt · Evolve with evidence.
Decide based on the results: keep a change that worked, refine one that partly worked, or rework an approach that did not fit. If the results are not yet clear, gather more observations. The insights flow back into how work is done, and the next round begins.
The foundation: Traceability, Control and Built-in Quality.
beacon platform connects traceable work steps, clear human decision points and integrated quality checks. That makes it possible to link observations, changes and subsequent results. Improvements are rolled out under control, and their effect is evaluated against the data captured.
The improvement is a hypothesis. Subsequent work provides the evidence.
Seat pricing
Seats for your organization
Contributor
Full multi-agent orchestration and build.
Reviewer
Write specs, steer an analyst agent.
Viewerfree
Read-only for stakeholders like board, sponsor, business unit.
Pricing from 10 users, on-prem, single-instance and org site licenses are negotiated separately.
Guided evaluation
Let's scope your environment.
Tell us your setup and constraints: on-prem or Swiss-hosted, team size, timeline. We'll come back with the next steps.