Machine Learning · 3 min read
AI · AI & Machine Learning
Applied AI that works inside your systems, not next to them.
We build applications with LLMs, RAG, agents and predictive models connected to the data and processes your company already runs, with measured evaluation, controlled cost and privacy by design.
Fig. 02 · Retrieval and generation pipeline
What it does
Extracts text from PDFs, DOCX and scans while preserving headings, tables and numbering.
Failure it prevents
Losing document structure, and with it the context of every passage.
Artifact
doc_id: contract-0442 pages: 37 · ocr: 4
Problems we solve
Most AI projects don't fail on the model. They fail on integration, on data, and on the lack of measurement.
ProblemApproach
The prototype impressed in the demo, but it makes up answers on real documents.
Hybrid retrieval, mandatory citations and explicit refusal when evidence is insufficient.
Every prompt or model change is a gamble, because nobody knows whether it got better or worse.
Versioned evaluation sets run in CI, with retrieval and faithfulness metrics.
Cost per request grows unchecked as usage increases.
Model routing by complexity, semantic caching, per-user limits and token observability.
Sensitive data can't leave the company environment without control.
Personal-data masking, deliberate provider choice and private-cloud deployment when required.
What we build
Custom AI development services, from assessment to production operation.
Custom AI applications
Products and features built around language models, with complete interface, backend and operations.
LLM integration
Commercial or open models connected to your systems behind an abstraction layer, so you can switch providers without a rewrite.
AI agents and workflows
Agents with tightly scoped tools, human approval at critical points and an audit trail for every action.
Retrieval-augmented generation
Search over internal documents and knowledge bases with verifiable citations and per-user permissions.
Document intelligence
Structured extraction from contracts, invoices, reports and forms, with validation and human review when confidence is low.
Predictive modeling
Demand forecasting, churn, credit risk and anomaly detection with honest time-based validation.
Machine learning pipelines
Ingestion, features, training, versioning and drift monitoring for models that need retraining.
AI in enterprise software
Integration with existing ERP, CRM, service desk and databases, respecting permissions and business rules.
Technical capabilities
What keeps an AI system working in production, beyond the model.
Continuous evaluation
Recall@k, faithfulness, refusal rate and regression tests on every change.
Hybrid search
Vectors combined with lexical search for exact terms, numbers and identifiers.
Guardrails
Structured output validation, content filters and action limits for agents.
LLM observability
Tracing of prompts, latency, tokens and cost per feature.
Provider independence
An abstraction layer that lets you swap or combine models.
MLOps
Data and model versioning, scheduled retraining and drift alerts.
Example applications
Typical scenarios where applied AI creates measurable value. These are scope examples, not delivered projects.
- Legal
Contract Q&A
Natural-language questions about clauses, deadlines and obligations, citing the source clause.
- Customer support
Ticket triage
Classification, prioritization and suggested replies based on history and the knowledge base.
- Finance
Invoice and document reading
Field extraction from invoices and bills, cross-checked against the ERP.
- Retail and manufacturing
Demand forecasting
Per-SKU, per-region models feeding purchasing and production, with confidence intervals.
Reference architecture
How we structure a RAG system with verifiable citations. Hover or tab through the components.
Components
- Contracts
- PDFs and DOCX, including scans.
- Parse + OCR
- Extracts text while preserving clause hierarchy.
- PII redaction
- Masks personal data before any external call.
- Clause chunker
- One chunk per clause, with the hierarchical path as metadata.
- Hybrid index
- PostgreSQL with pgvector for embeddings plus full-text search for exact terms.
- Question
- The user's natural-language question.
- Retrieve + RRF
- Vector and lexical search in parallel, merged with Reciprocal Rank Fusion.
- Rerank
- Reorders candidates and applies a minimum relevance threshold.
- Cited answer
- The model may only state what the cited passages support. No evidence, no answer.
- Eval set
- Reference questions with their correct clauses: measures recall@k and faithfulness.
Security and privacy
Enterprise AI needs the same rigor as any system that handles sensitive data, plus a few concerns of its own.
- Personal data and LGPD/GDPR
- We map what reaches the model, mask what isn't needed and document the legal basis with your team.
- Provider selection
- We review data retention, processing region and terms before any information is sent to an external API.
- Permission-aware retrieval
- Users only receive answers grounded in documents they are already allowed to see.
- Prompt injection
- Retrieved content is treated as data, never as instructions, and agents run with minimally scoped tools.
- Auditability
- Questions, sources used and actions taken are logged, with retention defined by contract.
How we implement
- 01
Use-case assessment
We define the problem, the available data and the metric that signals success.
- 02
Evaluation set
Together with your team we build reference questions and answers before writing the system.
- 03
Measured prototype
A first version evaluated against that set, with cost and latency measured.
- 04
Integration and security
Connection to real systems, permissions, data masking and guardrails.
- 05
Production and monitoring
Gradual rollout, quality and cost dashboards, and a continuous improvement loop.
AI questions
Do you train models from scratch?
Rarely worth it. In most cases the best result comes from existing models combined with retrieval, prompt design and, when there's enough data, fine-tuning. For tabular predictive problems we do train our own models.
Will our data be used to train third-party models?
We choose providers and plans that don't train on customer data and document it in the architecture. When requirements are stricter, we use open models hosted in your environment.
How do you know the AI is working?
With an evaluation set built together with your team before development. It measures whether the right sources were found and whether answers are faithful to them, and it runs on every change.
What does it cost to run an AI system in production?
It depends on volume and model choice. We estimate cost per query during the prototype and design controls such as caching, model routing and usage limits to keep spend predictable.
Do you work with open models?
Yes. We evaluate open models when there are privacy requirements, high-volume cost pressure or a need to run on your own infrastructure.
Related insights
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Have an AI use case in mind?
Describe the problem and the data you have. We'll reply with focused questions and a proposal for an assessment.
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