Model evaluation
Base model selection dictates the cost, latency, and capability of your final deployment. We evaluate candidates against your actual production data to establish a strong baseline.
General models are great, but they produce generalist agents
Off-the-shelf AI is useful, but lacks the specific context of your domain. True advantage comes from purpose-built agents trained on your specific workflows, proprietary expertise, and tailored models.
Hidden overhead of scaling AI
Transitioning from early adoption to enterprise-grade deployment exposes the inherent flaws of commercial APIs.
Inconsistent Task Execution
Off-the-shelf models struggle to maintain strict formatting, execute precise tool calls, or manage multi-step reasoning. Relying on increasingly complex prompts eventually leads to regressions and unpredictable outputs.
Escalating Inference Costs
General-purpose models force you to pay for massive parameter counts on every single query. Running high volumes of repetitive, domain-specific tasks through commercial APIs quickly degrades profit margins as adoption grows.
Restrictive Latency Bottlenecks
Heavy base models introduce inherent network and processing delays that degrade the user experience. This structural overhead prevents dynamic, real-time features from operating efficiently in live production environments.
Strict Compliance Barriers
Handling sensitive, regulated, or proprietary data requires complete architectural control. Routing private information through external vendor APIs introduces severe security risks and violates strict data residency requirements.
How we build specialized models
We apply a disciplined pipeline of dataset curation, model training, and continuous feedback to solve your exact use cases.
Core preparation
Dataset curation
Training data must mirror the exact edge cases your application handles. We synthesize historical logs, expert annotations, and distillation sets into high-quality pipelines.
Model training
Supervised fine-tuning
We train on highly curated input-output pairs to perfect strict formatting, tool calling, and classification, measuring every iteration against held-out production baselines.
Reinforcement fine-tuning
By replacing fixed examples with outcome-based grading, the model continuously adjusts its weights to reinforce successful behaviors and reduce errors in complex workflows.
Deployment and continuous improvement
Production inference
We validate latency, cost, and throughput before deployment, configuring the final endpoints with the exact access controls, monitoring, and fallbacks your infrastructure demands.
Continuous improvement loop
Live traffic reveals new edge cases. We capture these gaps for targeted retraining and deploy updates only once they clear strict regression tests.
Executing our fine-tuning approach on a real workload
InvoiceIQ is an 8B open model we fine-tuned for invoice extraction, then ranked against 13 frontier models on 494 real invoices.
- 80.16%
- Overall accuracy
- 70x lower
- Cost per run
- 82.59%
- Scanned documents
- +32 points
- Gain from fine-tuning
Third of the 14 models tested, behind Gemini 3.1 Pro.
$0.14 to run the full benchmark against $9.74 for the top model.
Second highest result, and ahead of Gemini 3.1 Pro.
The same base model scores 47.98% without fine-tuning.
Exact-match accuracy
Higher is better.
Gemini 3.1 Pro
InvoiceIQOur fine-tune
Claude Opus 5
Kimi K3
GPT-5.6 Sol
Qwen3 VL 8B InstructUntuned base modelCost of the full run
Lower is better.
InvoiceIQOur fine-tuneQwen3 VL 8B InstructUntuned base modelGemini 3.1 ProGPT-5.6 SolClaude Opus 5Kimi K3Improve what is already running
Common questions about fine-tuning a model that is already in production.