On-premise AI data analysis for sensitive spreadsheets
Deploy RowSpeak with Docker inside your own network, connect DeepSeek, GLM, or another open-source model, and turn finance and operations files into reviewable answers and reports without sending data to a public AI service.
Docker self-deployment • Fully air-gapped option • Deployment docs sent after a short form
Why Teams Choose Us
From private model hosting to a usable industry workflow
A private model endpoint alone does not reconcile a workbook, explain a variance, or produce a report that finance can review and sign off.
RowSpeak Private Deployment adds the workflow layer around the approved model.
It turns messy exports and multi-sheet workbooks into a repeatable industry workflow: ingest the file, run deterministic checks, apply a finance or operations template, and return evidence-backed output inside your environment.
With RowSpeak Private Deployment, your team can:
- Keep files, prompts, and outputs inside your approved environment
- Route simple tasks to an efficient open-weight model when quality and hardware checks pass
- Use industry templates for reconciliation, variance review, and recurring reports
- Keep calculations deterministic and show the evidence behind each answer
- Deliver output your team can review, correct, share, and use
The RowSpeak Solution
- Approved data boundary
- Swappable model layer
- Industry workflow templates
- Reviewable outputs
Why teams choose a private workflow agent
Keep sensitive data where it belongs
Process spreadsheets and reporting workflows inside your own environment instead of relying on a shared hosted setup.
Test the model against the workflow
Benchmark candidate open-weight MoE or approved API models on your real files, then route each task to the model that meets quality, latency, and cost requirements.
Fit existing IT and security requirements
Deploy RowSpeak in a way that aligns with internal review, access control, and infrastructure standards.
Package the work by industry
Start with one painful workflow—such as monthly reconciliation or budget-versus-actual review—and make its inputs, checks, prompts, and outputs repeatable.
Measure an outcome, not a demo
Use a proof of concept to compare review time, correction rate, answer quality, and cost per completed workflow before a wider rollout.

Self-hosted with Docker: deploy in your own environment
RowSpeak ships as Docker containers. Your IT team can install it with our deployment docs, or work with us remotely. Pick the boundary that matches your data policy.
On-Premise
Run RowSpeak on servers in your own data center. Files, prompts, model calls, and logs stay on infrastructure you control.
Fully Air-Gapped
Run RowSpeak and the model in an isolated network with no internet connection, for teams whose policy rules out any outbound traffic.
Private Cloud
Deploy in your own cloud account or private VPC when your approved environment is cloud-based.

Private LLM support: DeepSeek, GLM, and other open-source models
RowSpeak runs on top of an open-source model you host yourself, so no prompt or file content goes to a public model API. Test candidate models on your own files, then choose the one that fits your quality and hardware budget.
Model quality varies slightly between models and versions. Final results depend on your files and hardware, so we recommend testing on your own data first.
Why this matters:
On-premise vs cloud AI data analysis
Both use the same RowSpeak workflow. The difference is where your files and the model run.
| Question | RowSpeak Cloud | RowSpeak On-Premise |
|---|---|---|
| Where files are processed | RowSpeak cloud, in encrypted storage | Your own servers or private cloud |
| Which model answers | Models managed by RowSpeak | An open-source model you host, such as DeepSeek or GLM |
| Internet connection | Required | Optional. Fully air-gapped is supported |
| Setup | Sign up and upload a file | Docker deployment by your IT team, with our docs |
| Best for | Getting started fast with non-restricted files | Finance, HR, and regulated data that cannot leave your network |
Hardware requirements for self-hosted DeepSeek and GLM
Most of the hardware cost comes from the model, not from RowSpeak. The figures below are the minimum GPU setups published by the model makers and their recommended inference frameworks.
| Model | Size | Published GPU setup | Source |
|---|---|---|---|
| GLM-4.7-Flash | 30B (3B active) | 1× H100 (BF16) | Z.ai GLM README |
| GLM-4.5-Air | 106B (12B active) | 2× H100 (FP8) / 4× H100 (BF16) | Z.ai GLM README |
| GLM-4.5 / 4.6 / 4.7 | 355B (32B active) | 8× H100 (FP8) / 16× H100 (BF16) | Z.ai GLM README |
| DeepSeek V3 / R1 | 671B (37B active) | 1 node of 8× H200, or 2 nodes of 8× H100 / H20 | SGLang DeepSeek guide |
Figures come from official vendor documentation and assume FP8-capable GPUs such as NVIDIA H100/H200. Longer context windows and more concurrent users need more GPUs. Both model families also document support for Huawei Ascend and AMD accelerators. We confirm sizing for your workload in the deployment docs.
What your team can put into production
The deliverable is a controlled workflow that turns real business files into evidence-backed analysis and report-ready output.
Data Boundary
Model Choice
Workflow Fit
Reviewability
Your team can:
Start from real files
Use the spreadsheets and exports your team already reviews
Run repeatable checks
Check columns, totals, dates, duplicates, and missing values before analysis
Apply a workflow template
Reconcile, compare, explain, or summarize with a known business process
Show evidence
Keep source rows, formulas, assumptions, and corrections visible to reviewers
Export the decision pack
Share a report, chart, or dashboard after a human review
The opportunity is a repeatable result: less manual work, clear evidence, and a workflow the team can trust.
Built for teams with a repeatable, sensitive workflow
RowSpeak Private Deployment is a strong fit for organizations that:
Common teams include:
How deployment works: from Docker image to first analysis
1. Request the deployment docs
Fill in the short form. We send the deployment guide, architecture reference, and sizing notes to your inbox.
2. Prepare the server and model
Choose an open-source model such as DeepSeek or GLM and provision GPUs that match its published requirements.
3. Run RowSpeak with Docker
Start the RowSpeak containers inside your network, on-premise, in a private cloud, or fully air-gapped.
4. Test on your own files
Run a repeated workflow, such as a reconciliation or monthly report, on representative files and check the output.
5. Review and expand
Have reviewers sign off on the results, then roll out to more users and workflows.
Get the self-hosted deployment docs
Tell us a little about your environment and we will email the deployment guide for running RowSpeak with Docker.
RowSpeak Self-Hosted Deployment Pack
PDF • Updated regularly
Docker deployment guide
Reference architectures, including air-gapped
Hardware and model sizing guidance
Deployment and review checklist
Built for technical review and business adoption
RowSpeak Private Deployment is designed to help both technical and business stakeholders move forward with confidence.
Technical teams can review:
- Deployment architecture
- Data flow boundaries
- Environment ownership
- Model routing choices
- Infrastructure requirements
Business teams can evaluate:
- Reporting workflow fit
- Speed to usable output
- Reviewability of results
- Internal rollout potential
The goal is a reviewable workflow that technical, security, and domain teams can evaluate together.
Frequently Asked Questions
Is RowSpeak self-hosted?
Can RowSpeak run fully offline (air-gapped)?
Which open-source models are supported?
What hardware do we need?
Does any data leave our network?
How is this different from running a local LLM?
Can we get help with deployment?
Learn more about Private Deployment
See how a private workflow agent fits your environment
Evaluate a private RowSpeak workflow agent against your infrastructure, security requirements, model options, and first industry use case.
Start with one sensitive workflow, prove the result, and expand the private deployment when the evidence supports it.