The whole platform inside your contour
compliance out of the box — the data is stored and processed on your side
outbound calls — models, knowledge base and logs stay inside the perimeter
of the cloud version’s capability — the same platform, your perimeter
Parsewise is an AI assistant that answers on your company data. The on-premise build deploys the same platform inside your infrastructure: generation, search and dialogue history never leave the perimeter.
What banks and the public sector require: data, models and logs inside the network, role-based access, an audit trail on every step
An assistant that phones nowhere: generation, search and the knowledge base run on your servers — not a single request goes out.
The Parsewise team
Delivery architecture
The full delivery
The whole platform enters the contour, not a cut-down build. The same capability as the cloud: knowledge base, skills, connectors and analytics — only on your infrastructure.
Platform
The assistant, knowledge-base search, dialogues and analytics — the whole Parsewise core.
Local models
Generation and embeddings on models inside your contour — GigaChat, Qwen and others.
Admin
Knowledge base, skills, roles and dialogue history — managed inside your network.
Updates
Signed images from the release repository — updates with no vendor access to your network.
Closed by construction
Security requirements are met by architecture, not by promises. Role-based access, a complete action trail and zero outbound calls — the security team has plenty to audit and nothing to block.
Roles and access
RBAC plus corporate SSO and LDAP: everyone sees only their own sections and their own dialogues.
Audit log
Every action in the admin and every answer from the assistant lands in the event log, with its source and author.
Encryption
TLS inside the cluster and encrypted storage — data protected in transit and at rest.
No telemetry
The build sends the vendor neither metrics nor logs — not a single byte leaves.
152-FZ and GOST
Personal data is stored and processed inside your contour — compliance by construction.
Signed releases
Images are signed and verified on install — no foreign code enters the contour.
Speed inside the network
The models run next to the data. A request never goes out to the internet and never waits in someone else’s queue: the first token in fractions of a second, search in milliseconds, and throughput that scales with replicas.
Control without a trade-off
Two axes make the trade-off visible. Cloud bots take your data, in-house builds take years. On-premise closes both axes at once.
The top right is yours
Full platform capability and full data control at once — the position enterprises choose on-premise for.
Where the platform lands
Your contour in the centre, the delivery formats around it. Kubernetes, virtual machines, a fully isolated air-gap or a dedicated contour in a private cloud — the platform is the same in every format.
Documentation ships with it
Engineers get a process, not an archive of images. From the first helm install to the operating procedures — every step of the rollout is documented and proven on pilots.
Deployment guide
Step-by-step install: Kubernetes, virtual machines and air-gap — with sample configurations.
API reference
Every platform method with request examples — for integrations inside the contour.
Administrator guide
Knowledge base, roles, skills and analytics — running the platform day to day.
Operations runbook
Monitoring, backups, updates and recovery — procedures for your team.
The contour grows with the load
Replicas are added without stopping the service
A pilot inside the contour takes weeks, not years. The platform arrives assembled: models, search and the admin are already built — all that is left is connecting your data.
Discuss a pilot inside your own contour
A concrete conversation, not a deck. We will go through the tasks, the security requirements and the deployment plan — with the platform architects.
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