On-premise LLM

Fine-tune LLM models for your on-premise instances

Aukhtubut helps you adapt open-weight language models to your domain, your vocabulary, and your compliance constraints — then deploy them behind your firewall with the same agentic workflows you use in production.

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Why fine-tune on-premise?

Generic cloud models rarely match regulated industries, internal jargon, or document layouts. Running a specialized model on your own infrastructure keeps data sovereign while improving accuracy on the tasks that matter.

Data sovereignty

Training corpora, evaluation sets, and inference traffic stay inside your network. No customer PII or trade secrets leave your perimeter.

Domain accuracy

Adapt tone, terminology, extraction schemas, and reasoning patterns to banking, insurance, accounting, or your internal knowledge base.

Predictable cost & latency

Cap inference spend with dedicated GPUs, right-sized models, and batch-friendly workloads instead of per-token cloud APIs at scale.

Governed rollout

Pair model changes with Aukhtubut approval gates, execution history, and regression benchmarks before promoting a new checkpoint to production.

How it works — end to end

Aukhtubut wraps the full lifecycle — from raw business documents to a production-ready on-premise endpoint. Data preparation runs as a dedicated data pipeline (ETL / data engineering), not as agents; the AAI Engine orchestrates the higher-level flow and human approval gates around it. Your team stays in control at every step.

Reference lifecycle

  1. 1
    Scope & success criteriaYour team

    Define the target tasks (extraction, classification, Q&A, summarization), acceptable latency, languages, and compliance rules. These become pipeline parameters and evaluation rubrics.

  2. 2
    Corpus ingestionData pipeline

    A data pipeline pulls documents from object storage, databases, email, tickets, or the Business Console. Batch ingestion jobs land raw files in a staging zone and record provenance for audit.

  3. 3
    Pre-processing (data pipeline)Data pipeline

    Deterministic pipeline stages handle format conversion (PDF, Office, scans, OCR), text extraction, cleaning, deduplication, PII redaction, chunking, and language filtering. Reproducible, versioned, and testable — this is data engineering, not agent calls.

  4. 4
    Dataset constructionData pipeline

    The pipeline transforms cleaned data into training-ready formats — prompt/completion pairs, instruction datasets, or preference pairs — from your gold examples and policy manuals. Human reviewers validate samples through Kanban approval gates before a dataset version is frozen.

  5. 5
    Training orchestrationOn-prem stack

    LoRA, QLoRA, or full fine-tuning jobs run on your GPU cluster (bare metal, VMware, or Kubernetes). Checkpoints, hyperparameters, and logs are versioned and linked to the dataset version used.

  6. 6
    Evaluation & regressionOn-prem stack

    Held-out test sets and domain benchmarks score accuracy, hallucination rate, format compliance, and latency. Results are compared side-by-side with the baseline model inside the AAI Engine.

  7. 7
    Deploy to on-prem inferenceOn-prem stack

    Promoted adapters or merged weights are loaded into Ollama, vLLM, or a custom OpenAI-compatible endpoint. The AAI Engine registers the connection and routes agent nodes to your private model.

  8. 8
    Continuous improvementYour team

    Production failures and reviewer corrections feed back into the data pipeline. Scheduled pipeline runs rebuild fresh dataset versions and trigger retraining with the same governance gates.

Data pipeline — deterministic pre-processing

Fine-tuning quality depends on clean, representative data. Pre-processing is a dedicated, reproducible data pipeline (ingestion → transformation → validation → export) — not agents. Agents and the AAI Engine only orchestrate the pipeline and enforce human approval gates; the data transformations themselves are deterministic, versioned, and testable ETL stages.

  • Batch ingestion from MinIO/S3, databases, Kafka events, or Console uploads lands raw documents in a staging zone with recorded provenance.
  • Transformation stages — OCR, text extraction, cleaning, normalization, chunking, deduplication, PII redaction, language filtering — run as deterministic, idempotent pipeline steps.
  • Optional LLM-assisted labeling can be plugged in as a pipeline stage, but the core preparation stays rule-based and reproducible; Kanban approval gates require human sign-off before a dataset version is committed.
  • Dataset versions are stored with metadata (source, date range, approver, schema version, pipeline commit) for reproducibility and regulatory audit.
  • Synthetic augmentation — paraphrase, hard-negative mining, format variations — is an explicit pipeline stage bounded by your policies.
Ingestion (sources → staging)
  • Business Console records
  • Object storage & databases
  • Tickets & email
  • Policy manuals & runbooks
Transformation (ETL stages)
  • OCR & layout parsing
  • Cleaning & normalization
  • Chunking & dedup
  • PII redaction & filtering
Validation & export (artifacts)
  • Human review gates
  • Quality scoring
  • JSONL / Parquet exports
  • Version registry

Training methods & infrastructure

We match the technique to your hardware budget, base model size, and risk profile. Aukhtubut does not lock you into a single vendor stack — jobs run where your GPUs live.

LoRA / QLoRA adaptersRecommended default

Train small adapter matrices on top of frozen base weights. Fast iteration, lower VRAM, easy A/B between adapter versions on the same base model.

  • Typical bases: Llama 3, Mistral, Qwen 2.5, Gemma
  • Single-GPU friendly with 4-bit quantization
  • Hot-swap adapters in Ollama or vLLM without full model reload

Full fine-tuningMaximum domain shift

Update all weights when the domain diverges strongly from pre-training (heavy jargon, non-Latin scripts, proprietary formats). Requires multi-GPU and longer runs.

  • Best for small specialist models (1B–8B) on narrow tasks
  • Higher training cost; plan regression suites carefully
  • Merged checkpoints deployed as standalone artifacts

Preference alignment (DPO / RLHF-lite)Tone & policy

Align outputs with reviewer preferences — formal tone, citation style, refusal behavior — after supervised fine-tuning on task examples.

  • Pairs collected from Kanban review decisions in Aukhtubut
  • Reduces format drift and unsafe completions in regulated use cases
  • Optional stage; not required for pure extraction tasks

Where training runs

Jobs execute on your on-premise GPU nodes — NVIDIA DGX, consumer A100/H100 racks, or cloud-burst workers connected via VPN if you hybridize. Aukhtubut workflows invoke training containers (Axolotl, LLaMA-Factory, Unsloth, or your internal runners) through MCP or REST, passing dataset URIs and hyperparameter profiles. Secrets and model weights never pass through Aukhtubut SaaS unless you explicitly choose a managed training zone.

How Aukhtubut integrates

Fine-tuning is not a separate product — it extends the AAI Engine (Agentic AI), Business Console, and MCP connector layer you already use for production workflows.

MinIO / S3Raw documents, exports, checkpoint artifacts
MongoDBWorkflow definitions, execution history, dataset metadata
QdrantRAG corpora aligned with fine-tuned retrieval tasks
KeycloakSSO and role boundaries for data stewards vs operators

AAI Engine orchestration

The AAI Engine triggers and sequences the data pipeline, training, evaluation, and promotion steps — visible in the canvas, replayable from execution history, and stoppable from the live runner. It orchestrates; the pipeline does the data work.

LLM connection registry

On-prem endpoints (Ollama, vLLM, OpenAI-compatible proxies) are registered once. Agent nodes and evaluation jobs target the same connection profiles, so dev and prod stay aligned.

Human-in-the-loop by default

Dataset rows, promotion to production, and destructive retraining require Kanban approval — the same governance model as financial or compliance workflows.

Observable runs

Token usage, tool traces, training metrics, and evaluation scores are logged per execution for post-mortems and regulator-ready evidence.

Deploying on-premise inference

After evaluation sign-off, promoted models serve agentic workflows without sending prompts to public APIs.

  • Ollama — simplest path for edge and department servers; load merged models or Modelfiles with adapter instructions.
  • vLLM / TGI — high-throughput GPU serving with OpenAI-compatible `/v1/chat/completions` for the AAI Engine LLM nodes.
  • Air-gapped bundles — export model artifacts and workflow JSON for environments with no outbound network; upgrade via signed packages.
  • Blue/green promotion — run baseline and candidate models in parallel; route a percentage of workflow traffic to the candidate until metrics pass.
  • Embedding models — co-deploy text-embedding endpoints for RAG so retrieval and generation stay on the same sovereignty boundary.

Governance, security & compliance

Regulated teams need more than accuracy scores. Aukhtubut ties model lifecycle events to the same audit trail as business operations.

  • Role-based access: data scientists, reviewers, and production operators see different Console modules and workflow permissions.
  • Immutable execution history for who approved each dataset batch and which checkpoint is live in production.
  • PII redaction as a mandatory data-pipeline stage before data enters training; optional synthetic-only sandboxes for prompt engineering.
  • Regression gates: automatic block on promotion if eval metrics drop below thresholds you define.
  • Exportable reports for internal model risk management (MR) and external audit requests.

Typical engagement with Aukhtubut

We combine platform enablement with hands-on methodology — you retain ownership of models and data; we accelerate time-to-first-checkpoint.

Phased rollout

Most on-prem fine-tuning projects follow four phases over 6–12 weeks.

  1. Discovery workshop — tasks, data inventory, hardware sizing, compliance constraints.
  2. Pilot dataset & baseline — 500–2,000 curated examples, baseline eval, first LoRA checkpoint.
  3. Production hardening — approval workflows, monitoring, promotion runbook, operator training.
  4. Handover or managed iteration — your team runs retraining workflows, or Aukhtubut assists on a retainer.

Ready to specialize your on-premise LLM?

Tell us about your domain, data sources, and infrastructure. We will map a fine-tuning path that fits your governance model and plugs directly into Aukhtubut agentic workflows.

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