Model engineering
AI model fine-tuning
Smaller models, tuned on your data, that run inside your borders.
Small models, run locally, at around 5% of frontier cost.
~95%
Of frontier quality on your tasks
~95%
Lower inference cost
Local
Sovereign, in-boundary deployment
Ongoing
Automated re-tuning
Task quality
Inference cost
Latency
Indicative results on a scoped enterprise task set, measured per engagement.
Tuned small model against a frontier baseline: quality held, cost cut.
What makes it different
Small models that behave like large ones
On a defined enterprise task set, a well-tuned small model reaches roughly 95% of frontier quality. We prove it with an evaluation harness before anything is trusted in production.
- Task-scoped benchmarks built from your real cases
- Supervised fine-tuning, LoRA, and instruction tuning
- Side-by-side scoring against frontier baselines
Sovereign, governed, and local
The model runs inside your boundary. Training data never leaves, weights belong to you, and every training run is documented for audit and regulator questions.
- On-premise or in-country deployment and serving
- Documented data lineage and training provenance
- Access control and guardrails at the serving layer
Cheaper, and it stays that way
Right-sized models cut inference cost by around 95% and reduce energy draw. Automated re-tuning keeps quality from decaying as your data and processes move.
- Cost and latency modeling before commitment
- Sustainability gains from smaller compute footprint
- Scheduled automated fine-tuning with regression gates
What the engagement covers
Data curation
Collection, labeling, and quality control pipelines.
Fine-tuning
SFT, LoRA, and instruction tuning runs.
Evaluation
Task benchmarks scored against frontier baselines.
Sovereign serving
Local or in-country inference infrastructure.
Cost engineering
Quantization, batching, and right-sizing.
Automated re-tuning
Scheduled runs with regression protection.
How a model gets tuned
Scope
Define the task set and what good output actually means.
Curate
Build and quality-check the training and evaluation data.
Tune
Run training, then score against frontier baselines.
Serve
Deploy locally, monitor drift, and re-tune on schedule.
Key capabilities
- 01Dataset curation, labeling, and quality control
- 02Supervised fine-tuning, LoRA, and instruction tuning
- 03Arabic and multilingual model adaptation
- 04Evaluation harnesses, benchmarking, and guardrails
- 05Deployment, serving, and cost optimization
Enterprise readiness
Built and run to enterprise standards
Common questions
How do you reach 95% of frontier quality?
By narrowing scope. On your defined tasks with your data, a tuned small model closes most of the gap — measured, not assumed.
Where does the 95% cost saving come from?
Smaller models, local serving, quantization, and right-sized infrastructure instead of per-token frontier pricing.
Who owns the tuned model?
You do. Weights, data, and evaluation artifacts stay with your organization.
Where ambition
meets execution.
We help organizations make the next move matter — with technology that is practical, secure, regionally relevant, and built for adoption.