Fine-tuned specialist models that you own and run fast

When prompt engineering hits a ceiling on latency, unit cost or domain accuracy, we fine-tune and distill open-weights transformer, vision and multimodal models tailored to your exact task and served inside your VPC.

Scope Custom Model GPU & VRAM Bench
Deep learning LoRA fine-tuning, teacher-student distillation and quantized vLLM serving diagram deep.learning.blueprint curate · lora · distill · vllm 01 · CURATED DOMAIN CORPUS 2k–50k reviewed pairs · DPO Zero test-set contamination 02 · LORA & DISTILLATION 70B Teacher → 7B–14B Student Low-rank adapter weights 03 · QUANTIZED GPU SERVING (VLLM · TENSORRT-LLM · ONNX) FP8 / AWQ 4-BIT 155 TOKENS / S PRIVATE VPC GOLDEN EVAL GATE · OWN YOUR WEIGHTS · COST DOWN 60% Deployed inside your AWS, GCP or bare-metal GPU cluster with zero data egress PYTORCH · HUGGING FACE · TRL · VLLM · ONNX RUNTIME

WHAT WE BUILD WITH CUSTOM MODELS

Six reasons companies hire us to train and host custom models

When public APIs are too slow, too expensive or not allowed to see your confidential data, we train and deploy private models you own 100%.

Self-hosted LLMs inside your own cloud or servers Banking · Defense · Healthcare · Legal
SECURITY: Zero external API calls HOSTED: Private VPC vLLM · KMS encrypted
100% Private On-Premise / VPC AI

Self-hosted LLMs inside your own cloud or servers

Run Llama, Qwen, Mistral or DeepSeek inside your own AWS, GCP or on-premise GPU servers so zero customer data ever leaves your network.

Deploy a private LLM
Teach an open model your industry rules and exact formats LoRA / QLoRA + DPO Alignment
TASK: Regulated financial report drafting ACCURACY: 95.8% pass rate (beats generic GPT)
Domain-Specific Fine-Tuning

Teach an open model your industry rules and exact formats

Fine-tune a model on 2,000+ of your real cases so it follows your medical, legal, financial or engineering rules without a 3,000-word prompt.

Fine-tune a domain model
Cut OpenAI / Anthropic monthly spend by 50 to 75% 70B Teacher → 7B Student Distillation
VOLUME: 2.5M requests / month SAVINGS: Monthly bill cut from $9,400 to $2,100
Cutting High-Volume API Bills

Cut OpenAI / Anthropic monthly spend by 50 to 75%

Distill your most expensive, high-volume prompts into a compact 7B–14B specialist model that responds in 100ms at a fraction of token cost.

Cut our inference bill
Accurate speech-to-text for noisy audio, accents and jargon Whisper Fine-Tuning · Call Centers
INPUT: Noisy field & call-center audio WER: Word error rate reduced by 48%
Custom Speech & Audio Models

Accurate speech-to-text for noisy audio, accents and jargon

Fine-tune Whisper and voice models on your brand names, medical/technical terms and local accents (TR, DE, EN, AR) for call centers and field apps.

Tune a speech model
High-precision matching for SKUs, parts, CVs and clauses Bi-Encoder + Cross-Encoder Rerank
CORPUS: 250k industrial part specs RECALL: Top-5 match accuracy jumped 71% → 94%
Custom Search & Matching Encoders

High-precision matching for SKUs, parts, CVs and clauses

Train custom embedding and reranker models on your catalog or document pairs so search finds the exact match on the first try.

Train custom embeddings
Specialized visual detectors, segmentation and verification YOLO · U-Net · Siamese · VLMs
PIPELINE: Detection + U-Net + Siamese RUNTIME: Standardized on ONNX · < 25 ms
Custom Vision & Multimodal Models

Specialized visual detectors, segmentation and verification

Train custom YOLO detectors, U-Net pixel masks and Siamese verification networks for factory lines, signature forgery and medical/lab images.

Scope custom vision DL

GPU MEMORY & THROUGHPUT WORKBENCH

See what happens when we fine-tune, distill and quantize

Switch between an unoptimized 70B model, a LoRA-tuned 14B specialist and an AWQ-quantized 7B student running on vLLM.

GPU VRAM Footprint140 GB VRAM (2× H100 / A100 80GB)
Streaming Throughput28 tokens / sec
Time to First Token680 ms TTFT
500-Case Eval Pass Rate95.0% domain pass rate

Full 16-bit weights require multi-GPU tensor parallelism and high monthly cloud commit.

METHODS WE SHIP

Six neural engineering methods in our production toolkit

LoRA / QLoRA

Low-Rank Adaptation

Train <1% of parameters on domain schemas without catastrophic forgetting or multi-node GPU bills

Scope
DPO / ORPO

Direct Preference Optimization

Align tone, refusal boundaries and formatting on accepted vs rejected pairs without brittle RLHF reward models

Scope
Distillation

70B Teacher → 7B Student

Generate synthetic chain-of-thought rationales with 70B+ teacher models, filter by evals and train a fast student

Scope
Bi + Cross

Custom Embeddings & Rerankers

Mine hard-negative pairs from your catalog or legal corpus to push retrieval Recall@10 past 94%

Scope
U-Net / ViT

Pixel Segmentation & Encoders

EfficientNet-B3 + U-Net backbones for document cleanup, signature isolation and industrial defect masks

Scope
vLLM / TRT

PagedAttention & FP8 Serving

Continuous batching, KV-cache paging and ONNX/TensorRT compilation inside your private AWS or GCP VPC

Scope
Dedicated GPU infrastructure rack
100% weight ownership, zero data egress Every trained adapter and quantized checkpoint belongs to your company
Running a heavy prompt at volume?

Cut latency and GPU cost with a 3-week distillation sprint

We sample your production logs, verify a golden benchmark with senior engineers and train a compact student model.

Benchmark a fine-tune ↗

WHAT WE BUILD

Four core surfaces we deliver

01

Supervised fine-tuning (LoRA / QLoRA) and DPO

Adapt Llama, Gemma, Qwen or Mistral to your domain tasks and schemas.

We curate training pairs from your historical cases, run parameter-efficient fine-tuning and align outputs with Direct Preference Optimization (DPO).

LoRA / QLoRA SFTDPO alignmentLlama / Gemma / QwenReproducible training
Fine-tune a domain model
02

Knowledge distillation (Teacher → Student)

Keep 95%+ of frontier quality at a fraction of the size and latency.

Use a large teacher model (70B+ open model or frontier API) to generate and filter synthetic rationales, then train a compact 3B–14B student model.

Teacher-student pipelineSynthetic data filtering3B–14B compact models40–70% cost cut
Distill our heavy prompts
03

Custom embeddings, rerankers and multimodal encoders

Domain-tuned retrieval and audio/vision representation models.

Fine-tune bi-encoder embeddings and cross-encoder rerankers on your own terminology, part numbers and multilingual queries so search precision jumps.

Custom embeddingsCross-encoder rerankerWhisper / audio tuningVision-language adapters
Improve retrieval precision
04

High-throughput GPU serving (vLLM, TensorRT-LLM, ONNX)

Sub-second streaming inference inside your AWS, GCP or bare-metal VPC.

We quantize (FP8 / AWQ), batch and serve your models with vLLM or TensorRT-LLM, complete with autoscaling and automatic fallback.

vLLM / TensorRT-LLMFP8 / AWQ quantizationVPC self-hostingContinuous batching
Optimize model serving
4Diffusion pipelines in production
312GPU-hours, median fine-tune
0.94Validation accuracy, defect set
Validation accuracy across training epochs
  • TRAINvit-l epoch 12/20loss 0.31
  • DIFFUSEcontrolnet v3 sampling2.1s/img
  • EVALdefect set 0.94+0.01
  • QUANTint8 export18ms p95
  • DATA40k augments queuednightly

WAYS TO START

Three clear starting scopes

2–3 weeks

Feasibility & Distillation Benchmark

Test whether a fine-tuned small model beats your current prompt on cost and speed.

2 to 3 weeks
  • Curate 500–1,000 training & eval pairs
  • LoRA fine-tune on 7B/14B open model
  • Head-to-head accuracy, latency & cost report
  • Weights & eval harness delivered
Benchmark a fine-tune ↗
Most asked

Custom Fine-Tuned Model + VPC Serving

End-to-end dataset curation, SFT/DPO training and production vLLM deployment.

5 to 8 weeks
  • Dataset cleaning, synthesis & QA
  • Multi-run SFT + DPO training
  • Quantized vLLM / TensorRT endpoint in VPC
  • Shadow evaluation & CI regression gate
Scope a custom model ↗
Search lift

Domain Embedding & Reranker Sprint

Fine-tune your retrieval embeddings on your own catalog, docs or legal corpus.

3 to 4 weeks
  • Mine hard-negative query/document pairs
  • Fine-tune embedding + reranker pair
  • Recall@10 & MRR benchmark lift
  • Drop-in vector index migration
Boost search accuracy ↗

Adjacent disciplines

Where Deep Learning connects

Applied ML

Machine Learning

Classical tabular models, evaluation harnesses and feature engineering.

 Serving

MLOps & Governance

GPU autoscaling, drift monitoring, model registry and SLO gates.

 NLP

Natural Language Processing

RAG pipelines, entity extraction and multilingual language systems.

 Vision

Computer Vision

Custom object detection, U-Net segmentation and Siamese verification.

Curate · Train · Serve

Ready to own your model weightsand cut inference latency?

Share your task and volume under NDA. We reply with a training plan and fixed scope within one business day.

Scope a Fine-Tuning SprintSenior reply within 1 business day