Your organisation's data is already going into AI; the problem is that you don't know where
In many organisations today, employees paste contract text, customer data and internal procedures into general-purpose AI tools. They do it to get their work done faster, not out of bad intent. The result is not a discipline problem but a governance gap: the organisation can neither see this usage, nor record it, nor limit it.
In-house AI solutions exist to close that gap. The employee's need does not go away; it moves into a system the organisation can control.
We build systems that work with your organisation's own documents, follow your existing authorisation structure, log every query and show which document each answer is based on; the whole installation can also run on your own servers. We run the same architecture in our own products, so we are not building this system for the first time with you.
Where are in-house AI solutions used?
The scenarios below share the same core infrastructure; what differs is which documents they are connected to and who uses them. Most organisations start with a single department.
Enterprise knowledge assistant
People ask questions in natural language over procedures, regulations, technical manuals and project documents. We use a RAG and enterprise semantic search architecture, so the system grounds its answer in a specific document and shows that document instead of making something up.
Access to knowledge no longer depends on finding the right person to ask.
Document processing
Fields are extracted from contracts, invoices, quotes and tender files, and the result is written into your workflow. OCR handles scanned paperwork, so documents that arrive on paper are included too.
Less manual data entry and fewer missed clauses.
Human resources
Employees look up leave, personnel and benefits questions themselves, grounded in internal regulations, and HR is freed from repetitive questions. In CV pre-screening, candidates are summarised against the job criteria.
HR's time shifts from routine answers to real work.
Internal assistant for support teams
The system talks to the agent, not the customer: it suggests replies to incoming requests based on product documentation and past resolutions. The agent has the final word.
New agents get up to speed faster.
Procurement and contract analysis
Incoming contracts are compared with your standard template; deviating clauses, missing obligations and risky provisions are flagged. We developed this capability in our own contract analysis product.
Legal review starts from a marked-up text, not from scratch.
Finance and reporting
Long reports are summarised, period-on-period deviations are flagged and data is queried in natural language. Numerical results are linked to the source table, so every figure stays verifiable.
Questions can be asked without waiting for a report.
Technical teams and production
Fault records, maintenance history and technical documentation are queried from one place; similar past cases are retrieved to speed up root-cause analysis. Computer vision can be added for visual quality-control scenarios.
The experienced technician's knowledge stays in the organisation.
One platform, several departments
On a single platform each department gets its own document set and permission boundary, which is why the second and third use cases cost noticeably less than the first.
Each new use case is added on top of the platform already in place.
Let's work out together which use case will deliver results fastest for you.
Request an assessment for your organisationDeployment models: on-premise, private cloud and hybrid
This is the decision organisations get stuck on most, and the right answer depends not on technology but on your data policy.
| Criterion | Public cloud services | Private cloud | On your own servers (on-premise) |
|---|---|---|---|
| Where data is stored | On the provider's infrastructure, usually abroad | In a dedicated environment whose location is set by contract | In your organisation's own data centre |
| Does data go to an external service | Yes, with every query | Stays within the boundaries of the dedicated environment | No; it can run on a closed network |
| Hardware requirement | None | None; resources are rented | Requires investment in GPU servers |
| Setup time | Days | 2–4 weeks | 4–8 weeks (excluding hardware procurement) |
| Cost structure | Pay as you go; grows quickly with volume | Fixed monthly fee + usage | Upfront investment, then low variable cost |
| Model updates | Done by the provider; not under your control | Planned, together with you | Entirely on your schedule |
| Best-fit organisation | Teams with no sensitive data that want a quick start | Organisations that want contractual guarantees on data location | Public sector, defence, finance, healthcare and organisations required to run on closed networks |
We make the choice together: first a data classification is drawn up and we determine which document types can be processed in which environment. Many organisations end up hybrid: sensitive documents are processed on their own servers, the rest in a cheaper environment.
A realistic view of hardware: models in the 7–8 billion parameter class run on a single GPU with 24 GB of memory and are sufficient for many in-house scenarios. For the 30 billion class, plan for 48 GB; for the 70 billion class, two high-end GPUs. The vector database and application layer run on standard server hardware; the GPU is the main investment item.
Note: These are typical requirements for common open-weight models. The exact configuration we recommend for your organisation is given in the discovery output, after document volume and the number of concurrent users have been measured.
Data security, access management and KVKK compliance
Here are the answers an enterprise buyer is really looking for on this page.
Documents you are not authorised to see stay out of reach through the AI, too
This is the point that weighs most in an enterprise purchasing decision: does AI turn into a back door through the permission walls? The answer is not to rebuild permissions. Access rights are inherited from your existing identity management system; the retrieval layer never includes documents the asking user could not open in the result set. Because the model never sees that content, it cannot use it in an answer. When an employee's permissions change in the source system, their access on the AI side changes at the same moment.
We do not train models on your organisation's data. Your documents are not used to train general-purpose models, and this commitment is written into the contract. The system does not learn your data; it reads it when asked.
Logging, traceability and masking
Which user asked what, which documents the system used to produce the answer and what the answer was are all logged; this record is needed both for audits and to see where the system goes wrong. Fields that contain personal data can be masked before processing; which fields are masked is decided during data classification.
Answer sources and human approval
The system shows the documents behind every answer, and users can click through to read the original. When there is not enough support, it is designed to say "I couldn't find it" rather than quietly making something up. Human approval is mandatory at critical steps such as contract approval, payments and customer-facing correspondence.
KVKK and VERBİS
We build the system to support you in meeting your obligations under KVKK, Türkiye's Personal Data Protection Law: the data processing inventory, retention periods, access logs and documentation of the information you need for your VERBİS (data controllers' registry) filings. Our information security processes fall under ISO 27001.
Important: What is described here are engineering controls that technically support compliance. Legal compliance itself depends on your organisation's data processing activities and internal policies; this page does not guarantee legal compliance and is not a substitute for legal advice.
Our rollout process: from pilot to production
Most enterprise AI projects end not because of the technology but because they get stuck in what is called "pilot purgatory": the demo pleases everyone, but the system never enters the real workflow. We design the process to prevent that.
- Discovery and use-case prioritisation1–2 weeks
We set out in writing which problem will be solved in which department, and which ones should wait.
- Data inventory and preparation2–4 weeks
This is where projects actually get stuck. We map where documents are, which are current, which are duplicates and how access rights are defined.
- Pilot4–8 weeks
One use case, a limited document set, a limited user group. Success criteria are written and signed before the pilot starts.
- Move to production and training3–6 weeks
Integrations, authorisation, scaling and user training. If the pilot criteria were not met, we do not move to this stage.
- Monitoring and continuous improvementOngoing
Answer quality, usage rates and error patterns are monitored; the document set and prompts are updated accordingly.
What a success criterion looks like: "acceptable answer rate on 100 selected real questions", "average reduction in time to find information", "weekly active usage rate in the pilot group". We set the numbers together; writing the criteria afterwards makes a project unmeasurable.
Adoption is not left to chance. The most common reason a system goes unused is not that it is bad but that nobody knows how to use it. We name a champion user in each department and train people on their real work.
Integration with your existing systems
The system goes to where your documents are; it does not ask you to move them somewhere new. File servers, document management systems (SharePoint, Confluence and similar), ERP and CRM, email, ticketing systems and the data warehouse are all connectable sources.
On the identity side we connect to your enterprise identity management (SSO / Active Directory): the permission structure is not built from scratch but inherited from what you already have. A second permission list drifts away from the real one over time and becomes a security hole.
If the source system has no API, we solve it with a database view or a scheduled file transfer. For development needs beyond AI (interfaces, portals, integration services), our custom software development team works within the same project.
How is the return on investment measured?
Whoever has to get the budget approved will ask this, so we answer it up front.
- The metrics we measure: average time to complete a transaction, manual workload, first-contact resolution rate, error rate and time to find information. Which ones matter depends on the use case; for a knowledge assistant it is time to information, for document processing it is the error rate.
- The rule of measurement: the baseline is taken before the pilot starts. A "before" measurement taken afterwards is not a measurement; it is an estimate.
- Cost items: one-off setup and integration, hardware or usage fees, and maintenance and improvement. These three are shown separately in the proposal; you cannot calculate a return without seeing the total cost of ownership.
Case studies
Because our client deployments are covered by confidentiality agreements, we share our own production systems as examples that validate the architecture.
A source-citing enterprise knowledge assistant
- Problem
- Keyword search over a large text archive could not surface records that described the same situation in different words; users could never be sure they had found what they were looking for.
- Approach
- We combined a domain-trained language model with a RAG architecture and an agent system, tied every answer to the source it is based on, and made sure it does not answer when no support can be found.
- Result
- The system runs in production and serves more than 2,000 users; the same architecture is used in in-house deployments.
Clause and risk extraction in contract review
- Problem
- Reviewing contracts from different jurisdictions by hand was slow and carried the risk of missed obligations.
- Approach
- We built a system that extracts clauses, obligations and risks from contract texts, flags deviations from the standard template and runs multi-jurisdiction consistency checks. The final decision always stays with the lawyer.
- Result
- Review starts from a marked-up text, not a blank page.
Let's assess your use case together
In-house AI solutions differ from one organisation to the next, so the first meeting is not a product demo but an assessment of your scenario. The meeting is free and carries no commitment; if you prefer, we sign a non-disclosure agreement beforehand. We then prepare a written feasibility study covering the deployment model, scope and estimated timeline. Delivery is led by our Ankara-based AI team; for organisations that need an on-site installation, we come to you as a team.