SyscurContext engineering

Give AI the right records and instructions for each task.

The same rule, refined at every scale.Curate / Ground / Evaluate
Ground a model on your data

What we build

Everything that decides what the model sees.

01

Retrieval & grounding (RAG)

Connect the model to your real sources so answers cite your data, not the internet’s guess - and stay current as your data changes.

  • Retrieval over your documents and records
  • Answers grounded in your sources
  • Kept current as content updates
02

Knowledge base preparation

Get your content into shape for a model: structured, chunked, and cleaned, with the noise and duplication removed.

  • Structuring and cleaning content
  • Chunking and metadata
  • Removing stale and conflicting material
03

Evaluation & tuning

Measure whether the output is actually right, then tune the context and prompts until it is - and keep it there as things change.

  • Test sets and scoring
  • Prompt and context iteration
  • Regression checks over time

Why it works

Trustworthy output, measured and maintained.

Usable on the first pass

Grounded output means less rework and less second-guessing - the answer is right and traceable to a source.

Measured, not guessed

We evaluate against real examples, so “it’s better” is a number, not a feeling.

Stays honest

Context and prompts are checked as your data and needs change, so quality doesn’t quietly drift.

Getting generic answers from a capable model?

Give the model something real to work from.

Tell us what you want a model to answer and where the truth lives. We’ll scope the retrieval and grounding to make its output trustworthy.

Ground a model on your data