Search approved internal knowledge
Return the relevant passage and source instead of searching folders by hand.
Private AI and workflow automation
AI inside your workflow. Its data route visible.
Search internal knowledge, sort information, prepare work or assist a controlled process without placing every business record in a public AI account. A focused first build is usually £1,000 - £2,500.
On premises, privately hosted or hybrid. Hardware, hosting, electricity and outside providers are quoted separately.
Internal document helpersynthetic example
Approved staff request
Summarise the unresolved points in these service notes.
Approved notesPrivate modelHuman review
Draft onlyNo record changed and no outside model called
Source, model, permission and reviewer stay visible.
One jobbefore a general AI platform
Mappeddata, logs and external routes
Limitedknowledge, users and actions
Ownedupdates, monitoring and stop route
Interactive deployment comparison
Where does the request go? Follow every boundary.
Choose a synthetic route. The diagram distinguishes physical location, model provider and network access instead of treating every dedicated server as the same kind of private.
Synthetic architecture preview
01
Data journeyRequest to reviewed result
- 01Staff request→
- 02Local application→
- 03Local model and knowledge→
- 04Reviewed result
External boundaryNo external model API
A production diagram also names authentication, storage, indexes, logs, backups, monitoring and administrator access.
02
Decision viewFit and trade-off
comparedMaximum local control
Model and knowledge inside your network
Prompts, approved documents and generated output stay on equipment you control when external model calls, telemetry and remote logging are disabled.
- Good fit
- Sensitive internal search, drafting and classification where the available hardware can meet the job.
- Trade-off to own
- You own capacity, updates, backups, monitoring and electricity. Smaller local models may be less capable.
Start with work somebody can recognise
One useful job. Then prove it earns its place.
Useful for a business that wants internal document search, classification, drafting, extraction or a controlled workflow without placing every prompt and record in a general public AI account. A conventional rule-based automation may still be the better answer when the task is predictable.
Turn documents into structured fields
Prepare dates, categories or key facts for a person to check.
Prepare replies and summaries
Use selected records and templates without sending from an unrestricted mailbox.
Sort incoming work
Suggest a route, urgency or category while ordinary rules validate the outcome.
Add AI inside an existing tool
Expose one bounded capability through an API instead of introducing another dashboard.
Spot exceptions for a person
Bring uncertain, unusual or consequential cases to the owner rather than hiding them.
Private is a system property
The model can be local while its data still travels.
Putting a private model on a server does not decide which documents it may read, what it may remember, who can use it or which actions it may take. Without those boundaries, a local setup can still leak information, produce unchecked work or become an unmaintained second system.
The promise is written from the complete data map, not from the location of one model process.
AI taskModel callsDocumentsEmbeddingsLogsBackupsTelemetryRemote supportUpdates
Model output is a suggestion, not permission
Source, generate, validate, then act.
Start with one repeated, measurable job. Map the minimum data, choose an on-premises, private hosted or hybrid route, restrict sources and tools, then test ordinary use, hostile instructions, outages and recovery before real access is granted.
Approved data only
A role and purpose decide which records enter context.
Narrow task and output
The model receives the minimum context and produces a defined format.
Code checks the result
Schema, permissions, current state and business rules do not rely on generated prose.
A person owns consequences
Higher-impact changes stop for review and all actions remain attributable.
Choose by the job, not the badge
Local is not always better. Cloud is not always necessary.
Representative tasks are tested against the actual privacy boundary, quality threshold, waiting time and total operating cost before the deployment is named.
DecisionQuestion that changes the route
- Information
- Which fields are sensitive, personal, confidential or unnecessary?
- Capability
- What output quality passes a real acceptance set?
- Capacity
- How many people, documents and requests need a timely answer?
- Operation
- Who updates, monitors, backs up and restores it?
- Cost
- Is hardware and administration cheaper than measured provider use?
Proof stated at the right size
Real AI work. No invented private deployment.
Ernest runs four open-source AI setups on his own machines. Separately, three plugins of his own use AI on client sites for schema and FAQs, posting and backlinks, and article writing. There is no claimed client private-server deployment in the current Work library.
See all software and AI workCitable local experience
Open-source AI setups on Ernest’s machines
Direct operational experience, not a client count.
Built into working plugins
AI-enabled tools on client sites
Secure through the whole life cycle
Test the failure. Keep a stop route.
The UK AI Cyber Security Code and NCSC guidance treat secure design, deployment, operation and maintenance as one continuous job. Private infrastructure does not remove prompt injection, excessive permission, supply-chain or outage risk.
Private AI first-build price
A contained job with visible infrastructure.
The proposal names the purpose, representative tests, data route, model, sources, users, action permissions, infrastructure, handover and operating owners before implementation begins.
See all website and system pricesTypical focused setup£1,000 - £2,500usually 2 to 5 weeks after data and infrastructure review
- One bounded AI job and acceptance set
- Agreed model, data and network route
- One interface or focused system connection
- Failure tests, launch and owner handover
Price rises with new hardware, several data sources, authentication roles, image or audio models, high request volume, multiple actions, migration, difficult evaluation or regulated decisions.
Required Ernest licence£0/mo
Optional support can be quoted. Hardware, electricity, private hosting, backups and any outside model or monitoring provider are separate operating costs.
Before choosing a private model
Private AI questions.
Does private AI mean no data leaves the building?
Only an on-premises design with the relevant external routes disabled can make that boundary. The audit checks model calls, telemetry, logs, embeddings, backups, updates, remote support and monitoring. A dedicated hosted server is private in a different sense, but data still leaves the building.
Do I need to buy a powerful AI server?
Not automatically. Hardware depends on the model size, number of users, response time, document volume and whether the job includes images or audio. A small existing server may suit classification or retrieval, while heavier work may justify a GPU, private hosting or a hybrid route. The route is tested before hardware is recommended.
Will a local model be as capable as a cloud model?
Not for every task. Local models can be excellent for focused work with controlled knowledge, but larger cloud models may reason better on difficult or unusual requests. The page proposal compares quality, latency, privacy and total operating cost using representative work instead of assuming local is always superior.
Does the AI need to be trained on all our documents?
Usually not. Many useful systems retrieve relevant passages from an approved index and give them to an existing model at request time. That is different from training a model and is easier to update. Only information needed for the stated purpose should enter the source set, index, logs or evaluation data.
Can private AI update records or run parts of the business?
Yes, but local hosting does not make generated instructions trustworthy. Each tool gets minimum permissions, ordinary application code validates inputs and state, consequential actions require approval where appropriate, and the system needs monitoring plus a practical stop route.
Who maintains a self-hosted AI setup?
The proposal names owners for the server, model, source documents, user access, updates, backups, logs, tests and incident response. There is no required Ernest licence fee after handover. Optional support can be quoted, while hardware, electricity, hosting and any outside provider costs remain the client’s responsibility.
Bring one repeated task and three awkward examples
Map the data first. Then choose the model.
Show Ernest the work, the information it needs, the output a person would accept and the consequence of a wrong answer. You will get a straight view on local, hosted, hybrid or ordinary automation.
Discuss the first AI jobExplore every system and add-on Email or telephone only. No obligation and no model reseller pitch.