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Applied AI and agents

Classification, summarization, contextual search and suggested replies connected to the operation’s systems. AI suggests and prioritizes; the decision that matters stays with a person.

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What usually improves

  • Less time reading and sorting by hand
  • Context found in seconds, not in months of history
  • AI in production without losing human control
  • Sensitive data kept in your own environment

How much each point improves depends on the environment: volume, structure and data maturity. That is why no percentage is promised here. The assessment measures the current situation and defines, before any work starts, how the gain will be measured.

Diagnosis

Signs this solves your problem

  • Important information gets lost in chats, emails and documents.
  • The team spends time reading and sorting everything that comes in.
  • There is interest in AI, but also fear of it acting without control.
  • Sensitive data cannot leave the company’s environment.

Delivery

What gets done

  • Assisted communication hub

    Messages organized in a dedicated dashboard, with triage, history, tasks and suggested replies approved before sending.

  • Search and internal knowledge

    RAG, embeddings and semantic search over the company’s own documents and history.

  • Reading documents and screens

    OCR and information extraction to compare, classify and feed other processes.

  • Local models when needed

    Running in your own environment when privacy, cost or isolation justify it.

Most-used tools

  • LLMs
  • AI agents
  • OCR
  • RAG
  • Embeddings
  • Local models

Architecture

How AI stays under control

An example from the communication hub. Each layer has one responsibility and one constraint it cannot break. Control comes from the design, not from the good intentions of the code.

The rule that shapes the designAn incoming message is untrusted data, never an instruction. That single rule shapes the design of all five layers.

  1. Collection

    Responsibility: Captures and normalizes what comes in from the channel

    ConstraintSends nothing and takes no external action

  2. Local store

    Responsibility: Single authoritative source of history and state

    ConstraintAccepts no instruction that comes from a message

  3. Intelligence

    Responsibility: Classifies, summarizes, extracts pending items, suggests

    ConstraintNever writes to raw data and never decides what is sent

  4. Dashboard

    Responsibility: Human operation: triage, calendar, finance

    ConstraintStores no credentials and requires a token on every request

  5. Output

    Responsibility: Delivers the approved message

    ConstraintActs only on explicit confirmation, one message at a time

Projects

Related projects

Described anonymously: context, approach and outcome, without exposing the client or the implementation.

AI communication hub with human approval

Context
Conversations, requests and agreements from an entire messaging channel were stuck in the chat history, with no triage, no record and no way to find the context of an old decision.
Approach
Messages collected into a dedicated store, contextual history, triage with filters and tags, search for tasks and pending items, AI-suggested replies and manual approval before anything is sent.
Outcome
Communication organized in a single dashboard, with context that can be found and nothing sent without human confirmation.
See the layered architecture
  • Linux
  • Node.js
  • LLMs
  • Local APIs
  • Authentication
  • Logs

Automatic cross-system checks with OCR

Context
Decisions and identifiers shown in different systems, with no integration between them, had to be compared quickly to catch discrepancies.
Approach
Screen content captured with OCR, identifiers normalized and compared against rules, and inconsistencies flagged while the work is happening.
Outcome
Faster verification and an objective layer of checking, with discrepancy types classified to cut false alarms.
  • OCR
  • Python
  • UI automation
  • Business rules

Automated radar for technical updates

Context
Following several technology areas by hand meant too many sources and the risk of missing relevant updates.
Approach
A pipeline for collection, deduplication, classification, embeddings and relevance scoring, with daily and weekly reports written by a local model.
Outcome
Scattered monitoring turned into a structured technical intelligence process, isolated and validated before running on its own.
  • Python
  • SQLite
  • RSS and APIs
  • Embeddings
  • Local model

Contact

Recognize any of these signs?

Describe how the problem shows up day to day. The first conversation is for understanding the situation and saying honestly whether and how help is possible.

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