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ENRO
AI SOLUTIONS

AI Systems

Language, vision, prediction, voice, agents, and governance - we build AI systems that are explainable, monitored, and compliant by design, matched to the real problem.

LLM, RAG, ML
Disciplines
OpenAI, Anthropic, Google
Models
Evaluated and monitored
Governance
AI pipeline01 / 06
  1. 1
    DataPrivate sources, documents
  2. 2
    RetrievalSemantic indexing
  3. 3
    ModelLLM or classical ML
  4. 4
    EvaluationTests on every change
  5. 5
    GuardrailsHuman review, rules
  6. 6
    ProductionContinuous monitoring

Illustrative view

WHAT WE BUILD

Six AI disciplines, one engineering team.

AI means much more than chatting with a language model. We build across all six forms below, matched to the real problem.

Examples
  • LLM-powered assistants
  • Local RAG over private data
  • Vector search and semantic indexing
Tools
OpenAIAnthropicLangChainLlamaIndex
Examples
  • OCR and document intelligence
  • Image and video analysis
  • Object detection
Tools
OpenCVYOLOTesseractONNX
Examples
  • Forecasting and time series
  • Anomaly detection
  • Scoring and classification
Tools
scikit-learnXGBoostPyTorchpandas
Examples
  • Voice-to-text and transcription
  • Voice control
  • Speaker identification
Tools
WhisperWebRTCSpeaker ID
Examples
  • Personal agents with their own tools
  • Workflow automation
  • Operational AI dashboards
Tools
LangGraphn8nFunction calling
Examples
  • Model routing across providers
  • Evaluation pipelines
  • Production monitoring
Tools
DockerKubernetesMLflow
KNOWLEDGE RETRIEVAL

RAG over your private content, from source to answer.

Assistants that answer from your own documents, with verifiable sources and clear data boundaries.

1

Ingest

Private documents and data are taken in from the client's sources.

LlamaIndex
2

Index

Content is semantically indexed and prepared for vector search.

Vector search
3

Retrieve

For every question, the relevant passages are pulled back.

LangChainLlamaIndex
4

Generate

The model composes the answer from the retrieved passages.

OpenAIAnthropicGoogle
5

Verify

Answers are evaluated, and decisions with impact go through human validation.

EvaluationsHuman-in-the-loop
6

Operate

Quality and cost are tracked in production, on scalable infrastructure.

DockerKubernetes
TYPICAL DEPLOYMENTS
  • AI assistants and secure knowledge retrieval
  • RAG over private content
  • Customer support and internal sales enablement
  • Document intelligence for legal, pharma, and healthcare workflows
  • Product recommendations and operational insights
ENGINEERING DISCIPLINE

AI built to be verified.

Six practices we apply to every AI system we ship.

Controls active on every system6/6

Continuous evaluation

ON

Test suites that run on every model or prompt change.

Human oversight

ON

Human validation checkpoints for decisions with real impact, built in from the design.

Data governance

ON

Clear boundaries on what data reaches the model and where it stays.

Production monitoring

ON

Tracking response quality and cost, in real time.

Model routing and fallback

ON

Automatic switching between providers when a model is unavailable or too costly.

Explainability

ON

Decisions that can be traced back to their source, for audit and trust.

Have a problem that needs AI applied correctly?

Let's talk