Service · Custom & Specialized AI Models
Custom AI Model Development & LLM Fine-Tuning
A model built for your task beats a model built for everything.
When general-purpose AI is too slow, too expensive, too generic or can't leave your premises, Octobit8 fine-tunes, distils and trains specialised models that fit your data, your domain and your budget.
We'll tell you honestly if a well-engineered prompt or RAG system would get you there first. Custom models are worth it when the numbers say so — on accuracy, cost at scale, latency, privacy or language coverage.
Who is this for
Common profiles that get the most out of this service.
Accuracy has plateaued
Prompting and RAG have plateaued and the model still misreads your domain's terminology or formats.
Cost at scale
You run millions of calls a month and a smaller tuned model would do the job for a fraction of the price.
Latency-sensitive
Voice, real-time or on-device use cases need answers in milliseconds.
Privacy and sovereignty
Data can't be sent to third-party APIs, so the model must run in your own cloud or on-premise.
Consistency requirements
Outputs must follow a strict structure, tone or clinical, legal or brand style every single time.
Language coverage
You need strong performance in Hindi, other Indian languages or a low-resource language.
What sets it apart
Principles we apply on every engagement—so results are measurable, not just delivered.
Fine-tuned LLMs
Supervised fine-tuning and parameter-efficient methods (LoRA, QLoRA) on open-weight models such as Llama, Mistral, Qwen and Gemma, or managed fine-tuning on cloud platforms.
Preference-tuned models
Alignment with your experts' judgments using DPO and related techniques.
Distilled small language models
Compact models trained to reproduce a larger model's quality on your specific task, at much lower cost and latency.
Domain classifiers and extractors
Models for document classification, entity extraction, routing, triage and sentiment.
Embedding and re-ranking models
Custom retrieval models that make RAG far more accurate on specialised vocabulary.
Speech, vision and predictive ML
Fine-tuned speech recognition for accents and domain terms, vision models for documents or images, and classical ML for forecasting and risk.
Capabilities & deliverables
Concrete workstreams we plan, execute, and hand over with documentation and dashboards.
Fine-tuning & alignment
Adapting open-weight or managed models to your task.
- ▸LoRA / QLoRA parameter-efficient fine-tuning
- ▸Preference tuning (DPO) on expert judgments
- ▸Managed fine-tuning on cloud platforms
Distillation & classifiers
Smaller, faster, cheaper models for narrow tasks.
- ▸Distilled small language models
- ▸Classification, extraction, routing and triage models
- ▸Custom embedding and re-ranking models
Speech, vision & predictive ML
Beyond text, where classical or specialised models are the better tool.
- ▸Accent- and domain-tuned speech recognition
- ▸Vision models for documents and images
- ▸Forecasting, pricing and risk models
What you get
Concrete deliverables from this engagement.
Trained model weights that you own
Training data pipeline and data card
Evaluation report comparing the model with the baseline on quality, cost and latency
Production serving endpoint and monitoring
Retraining playbook
Additional benefits
How we work with you
A phased approach with clear artifacts—so stakeholders see progress weekly, not only at launch.
Feasibility
We define the task, success metric, baseline with off-the-shelf models, and the cost and latency target.
- Baseline benchmark
- Go / no-go recommendation
Data strategy
Collect, clean, label and, where useful, synthetically expand your training data, with privacy controls and de-identification.
- Training dataset
- Data card
Training and experiments
Systematic experiments across base models, methods and hyperparameters, all tracked and reproducible.
- Experiment log
- Candidate models
Evaluation
Held-out test sets, expert review, bias and safety checks, and head-to-head comparison against the baseline.
- Evaluation report
- Bias/safety review
Optimisation & deployment
Quantisation, pruning and serving optimisation, then serving in your cloud or on-premise with monitoring and a retraining pipeline.
- Serving endpoint
- Drift monitoring
- Retraining playbook
Use cases by industry
Where teams in our focus industries are already applying this service.
Travel & Hospitality
- ▸Demand and dynamic-pricing models for fleets, rooms and packages
- ▸Small, fast model for intent routing across millions of guest messages
- ▸Review and feedback classifier by topic, sentiment and urgency
Healthcare
- ▸Clinical note structuring and medical coding assistance
- ▸De-identification model that removes patient identifiers from documents
- ▸Specialised model for obstetrics and gynaecology documentation and patient education content
EdTech
- ▸Automated essay and short-answer scoring aligned to your rubrics
- ▸Question-generation model tuned to your curriculum and difficulty levels
- ▸Indian-language tutor model for regional-medium learners
Stack & integrations
Representative tools—we meet you where your stack already lives and document every handoff.
Training
- —PyTorch, Hugging Face Transformers
- —PEFT, TRL, Axolotl, Unsloth
Serving
- —vLLM, TGI, NVIDIA Triton
- —llama.cpp, ONNX
Managed platforms
- —Amazon SageMaker, Bedrock custom models
- —Microsoft Foundry fine-tuning, Vertex AI tuning
Tracking & classical ML
- —Weights & Biases, MLflow
- —Label Studio, Argilla
- —scikit-learn, XGBoost, LightGBM
Good to know
Data & licensing
- ✓Often needs less data than expected — a few thousand high-quality examples can work
- ✓You own the fine-tuned weights and training data, subject to the base model's licence, reviewed with you up front
Staying current
- ✓Designed for retraining — when a better base model appears, your pipeline lets you re-tune and compare quickly
- ✓Training cost is usually a small share of the total; savings come from cheaper, faster inference at scale
Engagement models
Model Feasibility Assessment
Fixed price, 2 weeks — benchmarks off-the-shelf models and estimates fine-tuning payback.
Fixed-scope Project
Milestone-based delivery to a production-served model.
Managed AI Operations
Monthly fee for drift monitoring, evaluation and retraining as usage grows.
Starter offer
Model Feasibility Assessment
2 weeks · [₹ / $ amount — confirm before publishing]
We benchmark 3–4 off-the-shelf models on your task and estimate what a fine-tuned model would deliver on quality, cost per 1,000 requests and latency. You get a clear go / no-go and a payback estimate.
Frequently asked questions
How much data do we need?▼
Who owns the model?▼
Will a custom model become outdated?▼
Is this expensive?▼
Often paired with
Ready to talk specifics?
Share your goals, timelines, and stack—we will propose a scoped next step.