For organisations that will not let their data leave the building, we build model infrastructure that runs on your own servers or in your own cloud tenancy: hardware planning, model selection, internal document search, routing that decides which job goes to which model, and cost measurement.
The first question facing organisations that want to use AI is not technical — it is about data. Will contracts, price lists, customer correspondence, technical drawings and production recipes be sent to an external service? In some organisations this is a preference; in others it is an obligation arising from a customer contract or a sector-specific sensitivity. Whichever the answer, the decision shapes the architecture from the outset, and changing it later is expensive.
Open-source models running in-house offer an answer to this question. Data never leaves the server, there is no per-use fee, and the model version stays under your control. But honesty requires stating the price: hardware investment is needed, models that can run in-house trail the most capable closed models, and maintaining the infrastructure becomes your responsibility. An on-premise proposal that leaves out these three points is incomplete.
In practice the most balanced outcome usually lands somewhere between the two. Most tasks can be done well enough by a model running in-house: internal document search, summarisation, classification, drafting. Meanwhile a small number of complex tasks need a stronger model — and those tasks usually contain no sensitive data. The routing layer we build makes exactly this distinction: which job goes to which model is set by rule, and requests containing sensitive data never leave the organisation.
The second part of this infrastructure is measurement. When nobody measures which department uses how much, what each job costs and how quality shifts over time, AI spending turns into a line item nobody owns. With measurement in place, the opposite happens: which uses genuinely create value, and which were tried and dropped, become visible. We deliver the deployment together with this measurement.
Companies with high sensitivity around contracts, prices and technical know-how. In these organisations the decision is usually commercial and legal rather than technical; the architecture is built to that decision.
Businesses whose contracts with their buyers include commitments about where data is processed. That commitment needs a counterpart on the software side; a statement of intent is not enough.
Organisations that want to take AI beyond a few people experimenting and roll it out across the business. At that scale, cost and governance become as decisive as model quality.
Businesses with large archives of technical documents, procedures and past correspondence. Making that archive searchable is the first and most tangible win in most organisations.
The hardware needed for the target usage volume and the total cost of ownership are worked out and compared with using an external service. The comparison is impartial, because every organisation has a threshold above which an in-house deployment becomes economical — and some organisations sit below it.
A test set is built from your real tasks and candidate models are compared on it. We look at the result in your work, not at generic benchmark tables. Turkish performance is assessed separately.
Company documents, procedures and archives are made searchable; answers are given with references to the source document. A setup that does not cite its sources earns no trust; users must be able to verify an answer. Permissions are preserved: a user cannot see the content of a document they have no right to access, even indirectly.
Which job goes to which model is set by rule: requests containing sensitive data stay in-house, and a stronger model is used when needed. Applications talk to a single interface; when a model changes, the applications do not.
Detection and masking of sensitive fields in outbound requests, prohibited-content rules and logging are put in place. Who asked what and which document they reached is traceable — a requirement for both security and audit.
Usage and cost are measured per department, user and application. Which use actually produces work becomes visible. Without measurement, the AI budget turns into a line item nobody can defend.
Which tasks will be given to AI and which data may never leave the organisation are pinned down. These two lists determine the entire architecture. The constraints come from the legal and commercial sides; we translate them into technical decisions.
An evaluation set is assembled from your real tasks. Candidate models are tried on it and the results compared. The set keeps serving afterwards too: when a model is updated, the same set confirms that quality has not slipped.
The hardware or cloud tenancy is prepared, the model serving layer is installed, and access and permissions are defined. The deployment is built so as not to be tied to any single model; swapping models later should be a configuration task.
The internal document index is built, permission controls are tested and routing rules are defined. Permission testing is a step that must not be skipped; search layers can, if left unchecked, leak unauthorised content through summaries.
Usage and cost measurement is switched on and user groups are added in stages. Maintenance responsibilities, the model update procedure and how to run the test set are handed over in writing.
In general, no — and we say so plainly. Models that can run in-house trail the strongest closed models. But for a large share of business tasks — document search, summarisation, classification and drafting, for example — they deliver sufficient results. The right question is not which is stronger, but whether it is sufficient for your work; we measure that with the test set.
It depends on the target model size and the number of concurrent users. We plan against a realistic usage estimate and compare against the cost of an external service. In some organisations the conclusion is this: usage volume is not high enough to make an in-house deployment economical. If that is the result, we say so; we do not try to talk you into building one.
In a fully in-house deployment it does not; the infrastructure runs on your servers or in your own cloud tenancy. In a hybrid architecture, which requests may go out is set by rule and sensitive fields are masked. Writing the rules correctly and keeping logs matter as much as which model is chosen; we build it to be auditable.
In most cases it is not needed, and we do not recommend it. The bulk of enterprise needs are met by a properly built document search layer, which is both cheaper and easier to keep current. Fine-tuning only comes onto the agenda when the search approach falls short and there is a well-defined, repetitive task. Even then, we put its cost and maintenance burden on the table openly.
Yes — we build the architecture to make that possible. Applications talk to a single interface; which model runs behind it is a configuration matter. Because this field changes fast, not locking into a single model is the deployment's most important design decision.
Model infrastructure running in your own environment; an evaluation test set built from your tasks and a model comparison report; permission-aware internal document search; the model routing layer and content rules; usage and cost measurement; and maintenance documentation. Everything, source code and deployment configuration included, is yours; the models run in your own environment.
In a 30-minute discovery call we listen to what you need and tell you honestly whether custom development or an off-the-shelf product is the better answer.
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