RAG & Agents
AI that answers with your organization's knowledge — not with guesswork.
Generic language models hallucinate, ignore institutional context, and don't know what happened yesterday. RAG fixes that. Agents go further: they act, consult sources, reason across multiple steps, and return grounded answers.
What you'll get
- Assistants and copilots that answer based on your actual documents
- Precise retrieval across large corpora — contracts, opinions, technical manuals, internal policies
- Agentic flows with traceability at every step
What we deliver
- RAG pipelines with hybrid search (BM25 + semantic)
- Agents with LangGraph and agentic frameworks
- Ingestion, chunking, and enrichment of specialized documents
- Vector stores (Qdrant, FAISS, pgvector)
- Integration with Azure OpenAI, OpenAI, Anthropic, and open-source models
- Reranking, metadata filters, and relevance control
Who it's for
Law firms, corporate legal departments, compliance teams, and companies with large document repositories — contracts, technical manuals, opinions, and internal knowledge bases.
Automation
Repetitive work consumes time from the people who should be solving complex problems.
Document triage, contract data extraction, opinion classification, draft generation — tasks that take hours can be automated with AI without giving up control or auditability.
What you'll get
- Less time spent on routine document tasks
- Standardized outputs without losing quality
- Auditable processes with a log of every automated decision
What we deliver
- Generation of standardized documents (contracts, drafts, opinions, communications)
- Structured information extraction (OCR + LLM)
- Intelligent classification and triage of contracts, proposals, and operational documents
- Integration with legacy systems via API or RPA
- Multi-step orchestration with LangGraph
Who it's for
Law firms, corporate legal departments, and companies with a high volume of standardized documents in operational routines.
Modeling & Machine Learning
Not every problem needs an LLM. Some need a model that actually learns from your data.
Document classification, prediction of contractual or operational outcomes, anomaly detection, risk analysis — problems with enough structure for supervised and unsupervised models that deliver accurate, explainable, and auditable results.
What you'll get
- Models trained on your data, not on generic data
- Predictions with clear, auditable performance metrics
- AI that complements human judgment in high-volume decisions
What we deliver
- Classification and prediction models (scikit-learn, XGBoost, LightGBM)
- Specialized NLP — entity extraction, classification, sentiment analysis
- Anomaly and pattern detection across large document and transactional volumes
- Time series for operational demand forecasting
- Fine-tuning of language models for specialized domains
- Training, validation, and deploy pipelines with monitoring
- Construction of annotated and synthetic datasets
Who it's for
Companies with enough data volume to train their own models — large law firms, legaltech, insurtech, fintech, and data-driven operations in general.