How Parsewise is built
How Parsewise is built
Four engineering pillars. RAG finds the knowledge, the LLM layer speaks to any model, function calling gives it hands and the security contour holds the borders.
One pipeline for every channel
The technology is invisible — what shows is an answer in a second. Inside, every question travels the same path, wherever it was asked.
A question in any channel
Widget, API or SDK — the customer’s sentence enters one pipeline.
Knowledge-base search
RAG finds the fragments by meaning in a fraction of a second.
The model with context
The LLM receives the fragments and the tools — and decides whether to answer or to act.
A streamed answer
First token under a second — the answer types itself into the dialogue.
Data goes in, answers come out. Inside sits the hot core of RAG, the LLM and function calling; around it, skills, connectors, the API and the security contour.
The whole platform on one map
Formats. Files, spreadsheets, audio and video are read as they are.
LLM modes. Function calling and standard prompting — for any model.
Contours. A Russian cloud, on-premise or a fully isolated air-gap.
Your data. Any model. Your contour
Three decisions that stay yours. The platform does not argue: it reads the data as it is, talks to the model you trust and lives in the perimeter you chose.
Milliseconds add up to a second
Every stage of the pipeline is on the clock. Which is why the customer sees the first character of the answer faster than they can blink twice.
First token
streaming into every channel
Search p95
semantic, across the whole base
Availability
the target service level
Scheduler
the background job cycle
Boring, reliable bricks
No exotica in production. Proven tools you are not afraid to answer for at three in the morning.
Vector store
pgvector: semantic search next to the main database.
Embeddings
Multilingual, 1024 dimensions — meaning instead of keywords.
SSE streaming
The answer types token by token — in the widget, the API and the SDK.
Kubernetes
Orchestration and automatic scaling under load.
Monitoring
Metrics and alerts on every node of the pipeline.
Backups
Regular copies and a rehearsed restore.
Look under the hood yourself
The best presentation of the technology is a working assistant. Assemble it on your own data in an evening
Tell us your task
Projects by type grow year over year
MVP, redesign, AI and support — cumulative
The studio profile across key axes
Speed, quality, transparency, engineering
Research, design and build overlap
Parallel streams — not a waterfall