Task and Achievement Definition
It is determined which user the model will serve, based on which data, within which limits, and with which acceptance criteria.
ENTERPRISE LANGUAGE MODELS
We build models that go beyond generic answers;
they understand your organization's knowledge, language and rules.
We prepare corporate data, combine it with the right model and information architecture; and make it usable in real business processes with authorization, source validation, security, and human control.
ENTERPRISE LLM SYSTEM · ACTIVEKNOWLEDGE → MODEL → CONTROL
INFORMATION IS NOT DIRECTLY TRANSLATED INTO THE MODEL · IT IS PREPAREJECT, AUTHORIZED, AND VERIFIEDRAG · ADAPTATION · GUARDRAIL · HUMAN
01 / MODEL STRATEGY
We define the corporate mission first, not the model.
It is not necessary to train the model from scratch for every need. Based on the use case, we establish the correct combination of base model, RAG, fine-tuning, tool usage, and operating environment.
Base Model Selection
A model family suitable for language, accuracy, context, latency, hardware, license, data security, and cost requirements is selected.
RAG or Fine-tuning Decision
RAG is used if up-to-date information is required; fine-tuning is used in a controlled manner if behavior, form, or expertise adaptation is required.
Operating Environment
Air-gapped local deployment, on-premises server, private cloud, or hybrid architecture is determined according to data and performance requirements.
02 / CORPORATE KNOWLEDGE LAYER
Before feeding information into the model,
we make it trustworthy.
We don't just upload corporate documents. We transform them into processable information assets with their source, owner, expiry date, confidentiality level, user authorization, and version.
- Source inventory and data ownership
- Cleaning, OCR, parsing, and re-checking
- Document type, subject, and confidentiality classification
- Parsing, metadata, and meaning vectors
- Authorization at the role, department, customer, and document level
- Version, validity, and deletion policies
KNOWLEDGE INGESTIONTRACEABLE
RAW SOURCEDocument · Record
Email · ImageSOURCE ID
Email · ImageSOURCE ID
PARSEOCR · Extract
Clean · DeduplicateCONTENT
Clean · DeduplicateCONTENT
CLASSIFYSubject · Privacy
Owner · ValidityMETADATA
Owner · ValidityMETADATA
ACCESSRole · Department
Document AuthorityPOLICY
Document AuthorityPOLICY
INFORMATION ASSUMPTIONChunk · Vector
Source · VersionREADY
Source · VersionREADY
RAW → CLEAN → CLASSIFY → AUTHORIZE → INDEXNO BLIND UPLOAD
03 / RAG AND SOURCE-BASED RESPONSE
The model does not use what it remembers,
but finds and uses an authoritative source.
The user's question first undergoes identity and authorization verification. The system only finds current pieces of information it can access; the model limits its response to these sources and shows the references to the user.
RETRIEVAL AUGMENTED GENERATIONSOURCE GROUNDED
ANSWER IS SHOWN WITH SOURCEIDENTITY · RETRIEVAL · CITATION
04 / MODEL ADAPTATION
Information with RAG,
behavior with controlled adaptation We develop the software layers on the device, from product startup to sensor management, communication to secure updates, along with the hardware.
Fine-tuning; used not to make the model memorize current organizational information, but to teach specific language, format, classification or expert task behavior. Training data is parsed, controlled and kept separate from the test set.
Turkish and multilingual terminologyInstitution's writing and response formatClassification and information extractionExpert task and field adaptationSynthetic data and human controlBase model / adapter version tracking
CONTROLLED ADAPTATIONTRAIN ≠ KNOWLEDGE STORE
SELECTED EXAMPLESClean · Labeled
Permitted · Balanced
Permitted · Balanced
EDUCATIONAdapter · LoRA
Task Tuning
Task Tuning
ADAPTED MODEL
AUTHENTICATIONHoldout · Red Team
Human Review
Human Review
RELEASEVersion · Rollback
Monitor
Monitor
DATASET → TRAIN → EVALUATE → RELEASENO UNCONTROLLED LEARNING
05 / LOCAL AND CLOSED-LOOP OPERATION
Artificial intelligence works internally.
Your data does not exceed the security boundary.
Sensitive data can be processed on the organization's mini PC, GPU workstation, or local server without being sent to the public cloud. Network, identity, encryption, and physical operation boundaries are designed together.
PRIVATE AI · CLOSED NETWORKNO PUBLIC CLOUD DATA
ATTACK ATTEMPT IS BLOCKED AT THE LIMIT AND SAVEDLocal GPU CLOSED NETWORK DATA SOVEREIGNTY
Network Isolation
A fully closed or restricted network with permitted connections reduces external service dependency and attack surface.
Identity and Authorization
User, application, and service identity is authenticated; each role only accesses authorized data and processing.
Encryption and Key
Data is encrypted in transit and on disk; keys are managed under institutional control and a separate security policy.
Firewall and Attack Prevention
Suspicious traffic is blocked at the gateway and firewall; the attempt is logged, an alert is generated, and it cannot reach the internal model.
Controlled Update
Model, application, and system updates are received from the authorized channel with signature, version, test, and fallback plan.
Audit and Evidence
Who used which document, received which response, and approved which action; can be tracked with a timestamp.
HUMAN-IN-THE-LOOP CONTROLAI CANNOT BYPASS
HIGH IMPACT THE PROCESS WILL NOT BE IMPLEMENTED WITHOUT HUMAN DECISIONREVIEW · APPROVE · REJECT · AUDIT
06 / HUMAN APPROVAL AND RESPONSIBILITY
Artificial intelligence suggests.
Authorized human makes the decision.
High-impact processes such as payments, contracts, official correspondence, customer record changes, financial transactions, personnel decisions, critical technical commands, and submissions to external systems will not be implemented without human approval.
Source and justification visibleRisk and transaction impact shownApprove, correct, or rejectDecision maker is recordedAuthorization matrix is mandatoryAI cannot bypass the approval gate
07 / AUTHORIZATION-SENSITIVE ARTIFICIAL INTELLIGENCE
The same question
does not reveal the same information to every user.
The model's access is limited by the user's role, department, client, project, document, and transaction authorization. Authorization is applied before seeking; inaccessible data is not given to the model as context.
Own Task Area
Only accesses information and documents related to their role and assigned processes.
Team and Operations
Views reports, processes, and pending approvals of the unit they are responsible for.
Sensitive Expert Data
Has controlled access to contract, financial, or audit data defined for the expert role.
Restricted Service
The customer or business partner only accesses their own account, document, and transaction context.
08 / TOOL USAGE AND INTEGRATION
The model doesn't just talk;
it moves the work forward with authorized tools.
We connect CRM, ERP, document system, e-mail, support, reporting, and workflow services to the model as controlled tools. Every tool call goes through parameter, authorization, risk, and outcome control.
AI ORCHESTRATORPLAN · TOOL · CHECK
CRM
ERP
DOCUMENT
EMAIL
SUPPORT
REPORT
WORKFLOW
API
09 / EVALUATION AND SECURITY TEST
We release not a good-looking demo,
but a measured enterprise system
The model is evaluated with test sets generated from real-world use cases; accuracy, resource commitment, authorization, security, latency, and transaction success are monitored separately.
Representative Test Set
Normal, difficult, incomplete, contradictory, and malicious questions are prepared with real role and document permissions.
Multidimensional Measurement
Response accuracy, resource suitability, access breach, hallucination, format, and task success are measured.
Attack and Escape Testing
Prompt injection, data leakage, authorization overshoot, malicious tool call, and manipulation attempts are applied.
Expert Review
A legal, financial, technical, or institutional expert evaluates critical outputs and acceptance thresholds.
Controlled Release
The previous model and information release begins with a limited number of users; it is expanded with a monitoring and feedback plan.
Stop off-source responsePrevent unauthorized information from being receivedClarify or redirect in case of ambiguityRequest human approval in high-risk situationsPrevent malicious tool callsMask personal and sensitive dataRecord response and transaction trailShow model and information version
10 / ENTERPRISE USE CASES
Not one model for everything—
task-specific solutions.
Each use case is designed as a separate enterprise solution with its own data sources, user roles, validation rules, human approval, and success criteria.
Internal Search and Q&A
Access to policy, procedure, product, project, and technical information with source attribution.
Document Reading and Processing
Classification, field extraction, comparison, summarization, and routing.
Contract and File Support
Article, risk, liability, and similar document analysis; legally approved draft.
Financial Information and Control
Report explanation, record matching, anomaly support, and controlled draft.
Support Assistant
Response suggestion, request classification, and process routing based on customer and product context.
Troubleshooting and Maintenance Assistant
Diagnostic steps and service recommendations from manual, device registration, and error history.
Institution-Specific Training Assistant
Supervised explanation and study support according to curriculum, content, and student role.
Workflow and Reporting
Request interpretation, task preparation, report drafting, and human-approved system processing.
11 / MODEL AND KNOWLEDGE LIFE CYCLE
After the model is released,
information and behavior are monitored together.
The model, prompt, adapter, knowledge index, policy, and evaluation set are released separately. Changes are tested, released in a controlled manner, and rolled back as needed.
YOUR CORPORATE LANGUAGE MODEL