Document intelligence
Extract decisions, entities, clauses, codes, and summaries from messy domain documents without sending them to external APIs.
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Custom Models
Models built around your workflow.
We design, train, fine-tune, and evaluate specialized AI models for tasks where generic chatbots are too slow, too expensive, too broad, or impossible to deploy with sensitive data.
Higher task accuracy with smaller models you can actually operate.
1 model objective tied to one measurable business workflow
24/7 availability in your own environment without cloud dependency
90% possible cost reduction versus broad cloud API usage for repeated tasks
The bottleneck
What this fixes
General-purpose LLMs are strong but unfocused. They carry high inference costs, unpredictable behavior, cloud data exposure, and more capability than a production workflow often needs.
Our work
How Tabularis helps
We turn your target workflow into a trainable specification: data, labels, evaluation sets, failure cases, deployment constraints, and acceptance thresholds. The result is a compact model tuned for the job, not a generic assistant with a long prompt.
Specific capabilities
Built for real production constraints
Domain classifiers for routing, triage, quality checks, risk detection, and compliance review.
Information extraction from contracts, invoices, forms, support tickets, medical text, and internal documents.
Fine-tuning and training from scratch when open models or API prompts are not enough.
Evaluation suites with golden datasets, adversarial examples, regression checks, and business metrics.
Multilingual workflows across European languages, including privacy-sensitive German documents.
Deployment packages for on-premise servers, VPCs, local GPUs, CPUs, or edge devices.
Engagement model
From first dataset to deployed system
01
Define the target
We turn the workflow into clear labels, outputs, latency goals, compliance constraints, and measurable success criteria.
02
Train the model
We combine your data, synthetic data, fine-tuning, and evaluation loops to create a model that fits the task.
03
Ship and monitor
We package the model for your infrastructure and set up checks for drift, quality, speed, and cost.
Where it pays off
Concrete use cases
Operational classifiers
Route cases, flag risks, prioritize queues, detect anomalies, and automate repetitive review steps.
Private copilots
Create assistants that understand your vocabulary, policies, and data boundaries while staying inside your environment.
Next step
Bring one workflow, dataset, or model target.
In the first call we map the technical path, data requirements, deployment constraints, and whether a focused pilot makes sense.