Discover & Scope
Align on problems, data reality, and success metrics. Opportunity brief, KPI model, phased roadmap, effort/cost ranges.
Model Fine-Tuning &
Optimization
Domain-adapted open-weights models that beat proprietary APIs on accuracy and cost — quantised, accelerated, and running on your own infrastructure.
Tailored AI models that cut costs without sacrificing intelligence
Relying on third-party API models for core product features introduces latency, high recurring costs, and vendor lock-in. Fine-tuning targeted open-weight models (like Llama, Mistral, or Qwen) allows you to achieve superior performance on specialized tasks while maintaining complete control over your data and infrastructure. We handle the entire fine-tuning pipeline: curated dataset curation, instruction tuning, DPO (Direct Preference Optimization), and domain adaptation.
Training the model is only half the battle. We optimize the trained weights for production deployment using quantization (AWQ, GGUF, FP8) and high-throughput inference engines like vLLM and TensorRT-LLM. The result is a specialized model that delivers lower latency and drastically reduced per-token costs compared to general-purpose closed models.
What We Do?
We curate domain-specific dataset pipelines, perform synthetic data generation, and fine-tune open-weight LLMs or specialized vision models for niche enterprise tasks.
We quantize and optimize models for production deployment, setting up high-performance inference servers on your private cloud or on-premise GPU clusters.
Key Deliverables
A fully fine-tuned, quantized model weights package and production inference pipeline optimized for high-throughput serving on your infrastructure.
- Curated synthetic and real dataset pipeline formatted for instruction/preference tuning
- Fine-tuned model weights adapted specifically to your business domain and task
- Quantized runtime deployment using vLLM or TensorRT-LLM for minimal inference latency
- Comprehensive benchmark suite comparing accuracy, speed, and cost against baseline models
What's included ?
- + Data curation, cleaning, and synthetic generation
- +Instruction tuning and preference alignment (DPO/RLHF)
- + Model quantization and inference speed optimization
- + Private GPU deployment and serving setup
Process
From Idea
to Production
to Production
FAQs
Frequently
asked questions
asked questions
A focused pilot reaches a working, testable v1 in four to six weeks. A production system - with on-site deployment and integration-typically runs eight to twelve weeks depending on hardware, data access and how many systems it has to talk to. We tell you which one you are in during discovery, not after.
A clear problem statement, a definition of success, access to sample data, and one stakeholder who can make decisions. That is genuinely it. We run a kickoff workshop to pin down scope and the KPI model before anyone writes code.
Whichever ones wins on accuracy, latency and cost for your problem. In practice: Claude and GPT for reasoning and generation; Llama and Mistral when it has to be self-hosted; YOLO, OpenCV and InsightFace for vision; PyTorch, ONNX and TensorRT for anything on the edge. We are not loyal to a vendor. We are loyal to the benchmark
Yes, and several of our systems do. We have shipped fully on-premise vision analytics that runs a single executable with no internet connection, and self-hosted voice assistants where every inference endpoint stays inside the customer VM. If compliance rules out third-party APIs, we design for that from the start.
Development is included in the project price. Model and API usage is billed at cost, based on your actual volume. We estimate it up front and then work to bring it down - compression, caching and smaller models where a smaller model is enough.
Monitoring, tuning and one support loop that runs from the engineers who built it. AI system drift-data changes, storefronts change, We watch for it and we fix it.