We turn your company's tacit knowledge —contracts, minutes, policies, manuals, emails, internal wikis— into accessible answers with AI. With the exact source cited in every response.
The standard clause requires written notice with 30 days' notice and includes a 5% penalty on the remaining contract if terminated without just cause.
In any company with more than a decade of operation, useful knowledge is scattered: SharePoints with thousands of files, shared network drives, scanned PDFs that aren't even text, meeting minutes only the executive assistant knows where to find, internal policies updated without communication.
The real cost of that fragmentation is paid every day. An in-house lawyer spending an hour finding the standard clause in similar contracts. An engineer reinventing a solution that already existed in a ticket closed two years ago. An executive making a decision without seeing the prior analysis their predecessor had done.
RAG (Retrieval-Augmented Generation) solves exactly that: it turns tacit knowledge repositories into a queryable layer, with natural-language answers and the exact source cited. Well implemented, it's one of the AI capabilities with the highest immediate return in any Panamanian company with history.
RAG is one of the AI capabilities with the highest immediate return in any Panamanian company with history.
From semantic search to a copilot specialized by function.
Search engine that understands query intent, not just keywords. Answers with the relevant passage, cites the exact source and lets you open the original document to verify context.
Converts scanned PDFs, contracts, minutes and invoices into actionable structured data. Extracts parties, terms, amounts, clauses and exposes them via API for the systems you already use.
Models that categorize incoming documents (correspondence, contracts, complaints, requests) and route them to the correct team without manual intervention.
Conversational interface that answers questions on internal policies, procedures and business knowledge, always citing the source. Ideal for onboarding, internal support and legal teams.
Specialized assistance for legal teams: compare contracts against template, detect atypical clauses, extract obligations and critical dates, and answer queries about the internal doctrine. Reduces review time without replacing the lawyer's judgment.
Our reference stack for RAG combines an ingestion pipeline that normalizes heterogeneous documents (native PDF, scanned PDF with OCR, Word, Excel, HTML, Markdown), an embedding engine with support for Panamanian Spanish and the client's sector terminology, and a generation layer with LLMs that answers with mandatory source citation.
Any format is ingested and normalized: native PDF, scanned PDF with OCR, Word, Excel, HTML, Markdown.
It's indexed with an embedding engine tuned to the client's sector vocabulary.
Every query retrieves the most relevant passages before generating any answer.
The LLM answers citing the exact source; it never invents without evidence.
On top of that base, the client team can define granular permissions per document and per role, so a user only receives answers from sources they are authorized to read.
A generic chatbot answers with what the model "knows", without access to your knowledge. RAG (Retrieval-Augmented Generation) first searches your documents, finds the relevant passage and then generates an answer based on that evidence. The difference is the source: in RAG every answer comes with a citation.
No, unless explicitly authorized. Standard deployment keeps both documents and the embedding index inside the client. Only generation may optionally use an external LLM, and always with filtered data; on-premise LLM is also available if the client requires it.
Yes. The pipeline includes OCR for scanned PDFs and images, with specific support for tables and customs documents. OCR precision is tuned case by case; on legible documents recognition exceeds 98%.
Yes. Models adjust to the sector and client vocabulary. For RAG in Panamanian banking we use local terminology; for logistics operators we incorporate customs jargon; for the legal sector we tune to relevant doctrine and jurisprudence.
6 to 12 weeks for a production deployment over a defined corpus (for example, legal contracts). Larger corpora or those distributed across multiple systems are structured in phases.
A 30-minute conversation with our team. Short discovery, no cost, no commitment.