AI opportunity discovery
We identify where AI creates measurable value and where a rule, integration or simple automation would be better.
VEI / CAPABILITY
Applied AI for real processes
Copilots, agents and automation where there is data, boundaries and supervision. We add AI when it improves a concrete task: finding information, assisting decisions, automating steps, analyzing documents or connecting tools with traceability.
Where it creates value
Technology is decided from operations, not from format.
AI is not treated as a separate service or a slogan. First we identify the work that hurts: document reading, classification, support, triage, reporting, system lookup, data extraction or operational decisions.
Then we design the right layer: semantic search, RAG, internal copilots, agents with tools, automation pipelines, evaluation, observability, costs and human approvals. If AI does not improve the process, we do not force it.
Capabilities
What must be resolved
Data, documents and context
We prepare sources, permissions, evidence, data quality and traceability so answers are useful.
Copilots and agents with tools
We design assistants that can query systems, prepare actions, orchestrate steps and request approval when needed.
Evaluation and control
We measure quality, errors, cost, security and usefulness to improve the solution with real usage.
Expected outcomes
A useful engagement changes decisions and operations.
Less time in scattered information
Reduce manual search, repetitive reading and preparation of responses or reports.
Automation with boundaries
Autonomy is introduced with permissions, guardrails, approvals and traceability.
Better decisions with context
Bring documents, historical data and internal systems closer to the point of decision.
AI that can evolve
Creates architecture ready to add new capabilities without rebuilding the product.
Working route
From uncertainty to an executable foundation.
- 01
Use case and data
We delimit task, users, risk, sources, permissions and expected outcome.
- 02
Solution design
We define experience, models, tools, RAG, flows, guardrails and evaluation criteria.
- 03
Integrated pilot
We build a first version connected to real data and systems in a controlled environment.
- 04
Measurement and scale
We observe usage, quality, costs and errors before expanding autonomy or scope.
Where it fits
Applied AI for real processes
Document AI with evidence
To query manuals, policies, contracts, SOPs or internal documentation with verifiable answers.
Internal operations agent
To classify requests, gather context, prepare actions and escalate complex cases.
Copilot inside a platform
To add assistance, summarization, extraction or recommendation inside software the team already uses.
Related editorial evidence
Public judgment before a commercial conversation.
Retrieving documents is not enough. A RAG system needs permissions, citations, evaluation, observability and a safe exit when it does not know.
Valtora is a VEI product built to deploy critical operations per client: assets, maintenance, incidents, document knowledge and AI in a dedicated installation.
Before predicting failures, more verifiable cases exist: finding documentation, classifying incidents and preparing decisions with context.
Frequently asked questions
Applied AI for real processes
01Do we always need to train our own model?
No. In many cases the value comes from integrating existing models, proprietary data, permissions, evidence and a strong product experience.
02How is an agent different from a chatbot?
An agent does not only answer: it can use tools, query systems, prepare actions and participate in workflows with defined boundaries.
03Can you add AI to existing software?
Yes, as long as the use case, data, permissions and architecture allow it.
04How do you control errors or risky answers?
With tool design, constraints, evaluation, observability, evidence and human approval when risk requires it.
VEI / NEXT DECISION