Agentic AI for deal delivery
Scale deal delivery. Not analyst workload.
In 6–8 weeks, we turn one document-heavy deal process into a production AI system using specialized agents, deterministic checks, and human review.
What Dotnitron does
Turn one costly deal process into a production AI system.
We connect documents, spreadsheets, firm knowledge, business rules, and reviewer judgment into a system the deal team can use.Map the process and baseline
Choose one expensive deal-delivery process, map its inputs and exceptions, then agree the current effort and acceptance criteria.
Explore this capability ↗Put specialized agents to work
Use agents to extract, compare, validate, and draft while deterministic checks and human reviewers control what moves forward.
Explore this capability ↗Shadow run, prove, and go live
Run the system beside the current human process, measure accuracy and review effort, then deploy with documentation and handover.
Explore this capability ↗The 6–8 week AI production sprint
From manual process to production in 6–8 weeks.
We map, build, evaluate, and deploy one system. The sprint ends with a working result and evidence for a go-live decision.Build
Engineer the complete workflow.
We connect models, retrieval, business logic, interfaces, integrations, and review queues around the way the team already operates.Where we create value
Built for deal teams. Proven in adjacent expert work.
Our focus is PE and M&A delivery. The same method applies to diligence, compliance, legal, finance, and other document-heavy work.Private equity
Diligence, portfolio intelligence, and value-creation work buried inside documents and fragmented operating data.
Advisory and consulting
Delivery margin lost to evidence intake, workpaper preparation, research, and repeated reviewer reconstruction.
BFSI, risk, and compliance
Operational answers and compliance work that must respect access, residency, auditability, and control boundaries.
Legal and compliance
Contract, policy, and obligation review where every conclusion needs defensible support.
Delivery evidence
Real delivery. Clear evidence boundaries.
See what we built, our role, the operating context, and what the available evidence can support.
Production Enterprise AI
Enterprise generative AI platform across seven regional environments
Contributed retrieval, document processing, model integration, product engineering, testing, and production support across seven separately maintained regional environments.- 7 Regional Environments
- ~3,500 Registered Users
- OpenAI + Azure OpenAI
Production Private Markets AI
Private-markets intelligence platform deployed to five enterprise clients
Built frontend, backend, document retrieval, and multi-model capabilities for a private-markets platform deployed in five customer-controlled environments.- 5 Enterprise Deployments
- Customer-Controlled Infrastructure
- Multi-Provider AI
Validated Enterprise AI
Compliance and risk platform validated before deployment
Delivered an AI-enabled compliance and risk-management platform end to end. The customer has validated the build and uses it in prospect demonstrations while the first end-client deployment remains pending.- Validated Pre-Deployment
- End-to-End Delivery
- OpenAI + Azure OpenAI
- Source visibility or inspectable SQL
- Human approval before consequential action
- Role-based access and scoped tools
- Evaluation evidence before expansion
Capability layers
Reusable foundations. Firm-specific systems.
Underlying, SemeLabs, and our data-readiness layer reduce build time. The result still fits your sources, methodology, controls, and templates.Underlying
A reusable Dotnitron capability for agentic analysis of diligence files, policies, contracts, evidence, reports, and data rooms where outputs need source visibility and human approval.
Explore layer ↗ERP and Operating Data Answer LayerSemeLabs
A reusable Dotnitron capability for SQL-backed answers from approved ERP, finance, and operating data when teams need visible logic and validation.
Explore layer ↗Data Readiness LayerPelestra
A reusable Dotnitron capability for preparing messy, sensitive repositories before AI touches regulated enterprise data.
Explore layer ↗How an engagement starts
Start with one process worth proving.
Repeated analyst effort, a named owner, representative inputs, a known review standard, and a delivery outcome the firm can measure.
- 01
Map one costly deal process
Bring a repeated process, representative samples, and the people who own the outcome. Sanitized material is enough to start.
- 02
Build one production path
We engineer the multi-agent system around approved sources, real outputs, reviewer control, and the client’s deployment environment.
- 03
Measure, operate, and expand
Once the path proves useful, we support adoption and extend the operating pattern into adjacent workstreams.
Recognition supporting our work helping organizations build, deploy, and scale AI solutions with OpenAI.
Deployment confidence
Approved data boundaries.Reviewable by people.Measurable in production.
Review our security approach ↗Questions senior teams ask first
What decision-makers ask first.
What does Dotnitron do?
Dotnitron helps transaction advisory teams redesign and automate analyst-heavy deal processes. We combine AI agents, software, deterministic checks, integrations, and human review around the firm's methodology, tools, controls, and deliverables, then validate the system before go-live.
Why start with transaction advisory deal delivery?
Deal delivery contains repeated, expensive, document-heavy work with clear outputs and senior review. That makes VDR triage, diligence extraction, workpaper preparation, and management Q&A valuable enough to automate and concrete enough to validate.
Is Dotnitron an AI agency or a software product?
Dotnitron is a specialist AI implementation partner. We use reusable capabilities such as Underlying and SemeLabs to accelerate delivery, but the client buys the outcome: a working production system adapted to its data, methodology, controls, users, and output templates.
Do you build AI agents?
Yes. Depending on the process, we may use one agent or coordinate multiple specialized agents for extraction, comparison, validation, drafting, and review preparation. They operate inside approved scopes, tool permissions, source retrieval, logging, and human approval points.
Which firms are the best fit?
The strongest fit is a partner-led transaction advisory team with a repeated analyst-heavy process, a named business owner, representative source material, and reviewers who can define acceptable output. That can be a specialist firm or a focused practice inside a larger organization.
How do you reduce the risk of wrong AI answers?
We design scope and controls before rollout. Outputs are tied to source documents, visible SQL, approved data scopes, reviewer checkpoints, and pass/partial/fail validation evidence.
Do you replace human reviewers?
No. We remove repetitive preparation and analysis bottlenecks. Human reviewers still inspect, edit, approve, and decide what becomes operationally or client-facing.
Can this run in a private environment?
Yes. We can deploy agentic systems in private cloud, tenant-isolated, or client-approved environments with role-based access, audit trails, and data isolation.
What is the best first deal process to automate?
Start with one process that repeats across mandates, consumes meaningful analyst time, has recognizable inputs and outputs, and can be judged against current human work. VDR review, diligence extraction, workpaper drafting, management Q&A, and document-to-structured-data systems are strong candidates.
What happens on the first call?
The first call is a system-fit conversation. We identify the process, current analyst effort, source types, tools, reviewers, desired output, data sensitivity, and acceptance criteria. We then decide whether a 6–8 week Agentic AI Production Sprint is the right next step.
Start with one costly deal process.