Compliance and evidence workflows
SOC 2, ISO 27001, control mapping, gap analysis, evidence review, ToD, and ToE support where outputs need reviewer approval.
Forward-Deployed AI Engineering
Forward-deployed AI engineering for real operating workflows.
We work close to business users, operators, analysts, reviewers, and technical owners to map one urgent workflow, build the system, validate outputs, and move from AI experimentation to operating change.
What is Dotnitron? Dotnitron is a forward-deployed AI workflow implementation company. We help teams turn AI models, agents, internal data, documents, ERP systems, and human review processes into secure production workflows with measurable operating value.
01
Deployment is the hard part now
Model access is no longer the bottleneck. The bottleneck is turning model capability into reliable systems connected to messy data, legacy tools, confidential documents, security controls, and human decision paths.
02
We embed with the workflow, not just the tech stack
Forward-deployed work means sitting with the people who run the process, understanding where interpretation happens, where delays compound, which outputs matter, and which risks must be controlled before any AI system can be trusted.
03
We build from one priority workflow
The first sprint should be narrow enough to ship and valuable enough to matter. We identify one recurring bottleneck, define data and review boundaries, build a working path, and validate the output against real examples.
04
Small team speed, senior ownership
The early engagement is led by senior technical operators, not a rotating workshop team. That matters when the buyer needs a working pilot, a validation report, and a clear production path before budget or timeline disappears.
05
We turn field signal into reusable patterns
Dotnitron uses InsightGale for document workflows, SemeLabs for ERP answer workflows, and Pelestra for data readiness. These engines let us reuse proven delivery layers while adapting each implementation to the client's methodology.
06
The outcome is durable operating infrastructure
A successful engagement produces a production-ready workflow, validation report, adoption path, expansion map, and clear ownership model. The goal is not a workshop. The goal is daily use.
Where This Applies
Start where repeated interpretation work is already hurting margin.
The best AI agent implementation projects do not begin with a blank transformation program. They begin with a workflow that already has volume, rules, exceptions, reviewer judgment, and measurable delay.
Diligence and verification workflows
Data-room review, background verification, secretarial due diligence, and other document-heavy processes that need source-backed findings.
ERP and operational answer workflows
SQL-backed operational intelligence for teams that need recurring answers from ERP and source-system data without analyst queues.
FAQ
Frequently asked questions
What is forward-deployed AI engineering?
Forward-deployed AI engineering embeds technical builders close to business teams to design, build, and deploy AI systems inside real workflows rather than handing over generic advice or disconnected prototypes.
How is Dotnitron different from a traditional AI consultant?
Dotnitron is implementation-led. We map the workflow, build the software, integrate the data/tools, define validation, and support rollout instead of stopping at strategy.
Who is this best for?
It is best for professional services firms and mid-market operators with recurring document-heavy, compliance-heavy, diligence-heavy, verification-heavy, or ERP-heavy workflows that need production systems quickly.
Ready to move beyond AI pilots?
Bring us one workflow where an AI agent could remove repeated analyst work without losing source visibility, controls, or human review.