CapabilitiesApplied AI
Assessment, engineering, and adoption work for teams putting AI into real workflows, with judgment and accountability intact.
On every engagement
Most accurate AI research on the public benchmark, ahead of Google, OpenAI and Perplexity.
From brief to report. The assessment starts from evidence, not a vendor list.
Report figures cited, each checked against its source by code.
How it runs
Map the opportunity. Workflows mapped against value and feasibility. Build, buy, and wait calls made explicit, with risk and governance in view from the start.
Build the first working version. Engineers embedded with your team, shipping production-grade tools and AI workflows in your environment, on your data, under your controls.
Make it the way the team works. Workflow redesign, training, usage measurement, and guardrails. Adoption treated as an operating change, not a software rollout.
Measure what changed. Outcomes tracked against the baseline: time, cost, throughput, error rates. What is not moving gets fixed or stopped.
Leave the capability behind. Reusable models, decision logic, and workflow patterns stay with your team, so the next deployment starts ahead.
Lunon supports the research, analysis, and strategic workstreams that shape an investment, client recommendation, or major operating decision.
Three questions come to us under this heading: where AI would create measurable value, who builds the first working version, and how it becomes the way the team actually works.
One method. Every question.
What we run
Where would AI create measurable value here, and where would it only add noise?
Workflows mapped against value and feasibility. Build, buy, and wait calls made explicit, with risk and governance in view from the start.
Typical triggers · An AI mandate without a plan · Scattered pilots · Platform decisions
Who builds the first working version?
Engineers embedded with your team, shipping production-grade internal tools and AI workflows in your environment.
Typical triggers · Internal tooling · Workflow automation · A proof that must run in production
How does this become the way the team actually works?
Workflow redesign, training, usage measurement, and guardrails. Adoption treated as an operating change, not a software rollout.
Typical triggers · Post-deployment stall · Low usage · Governance requirements
Recent work
Engagements described plainly. Clients are not named.
Complex enterprise · Forward-deployed engineering
Production-ready
Embedded workflow
Engineering embedded in the operating problem to turn an AI pilot into a production workflow with real users, data, and controls.
Your material
Under NDA from the first call.Every engagement begins under NDA, with binding terms on confidentiality, data handling, deletion at close, and no training.
Yours, and deleted at close.Material is scoped to the engagement, hosted in the United States, retained only for the life of the work, and returned or deleted at close on your instruction.
Never used to train models.Lunon does not use your documents, data, or deliverables to train models. Our model providers are contractually bound to zero data retention and no training.
Isolated and audited.Every engagement is its own boundary, access is limited to the team on your engagement, and every access is written to an append-only audit trail. SOC 2 Type I, with Type II underway.
Tell us what your team needs to understand, evaluate, or deliver.
