SIGMATORQ
  • Improve accuracy
  • Reduce cost

We improve and train the AI models your business runs on.

Sigma Torq makes enterprise AI measurably more accurate and cheaper to run. We benchmark the system you have today, fix the models and data holding it back, then fine-tune or fully retrain them on your domain. Every gain is proven on your data, never on a public leaderboard.

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What we measure

Retrieval precision, answer quality, and regression over time

Where we work

Your corpus, your stack, your latency and cost budget

What you keep

The trained model, the datasets, and the harness to rerun it

Services

Four places enterprise AI usually breaks

Most systems do not fail because the model is weak. They fail because nobody has measured which part is weak. We work the whole chain, from the documents going in, to the model doing the reasoning, to the agent acting at the end.

Evaluation & benchmarking

Know how good your system actually is before you spend a quarter changing it.

  • Golden test sets built from your own documents and real user questions
  • Domain-specific grading rubrics instead of vague “relevance” scores
  • Human-reviewed relevance labeling at corpus scale
  • Verification passes, so a score means the same thing twice

RAG & retrieval optimization

If the right passage never reaches the model, no amount of prompting saves the answer.

  • Ingestion and chunking strategies matched to your document structure
  • Embedding model and vector database selection, benchmarked not guessed
  • Hybrid search and reranking tuned against your labeled set
  • Measured retrieval lift, reported per query class

Model training & fine-tuning

When prompting and retrieval run out of road, we train the model itself on your domain.

  • Fine-tuning and post-training on your own data
  • Proprietary models trained from the ground up where an API model will not do
  • Training sets built from synthetic generation grounded in your corpus
  • Human-in-the-loop labeling with auditable correction ledgers

Agentic infrastructure

Demos are easy. We build the agent plumbing that survives real traffic and real edge cases.

  • Agent pipelines and orchestration with sane failure handling
  • Tool and API integration against your internal systems
  • Batch and long-running jobs that resume instead of starting over
  • Production deployment, observability, and cost control

Approach

Measure, diagnose, improve, verify

The same four steps every time. The order matters: we do not change or retrain a system before we can tell whether the change helped.

  1. STEP 01

    Baseline

    Build a test set from your data and score the system as it stands today. No baseline, no claim.

  2. STEP 02

    Diagnose

    Separate retrieval failures from reasoning failures from data problems, and rank them by cost.

  3. STEP 03

    Improve

    Fix the top failure mode: chunking, reranking, better data, or training the model itself.

  4. STEP 04

    Verify

    Re-score against the same set, report the delta honestly, and hand you the harness to keep using.

Why Sigma Torq

We would rather show you a number than tell you a story

Evidence before changes

Every engagement starts with a measurement you can inspect. If we cannot measure the problem, we will tell you that instead of shipping a guess.

Trained on your data

Public benchmarks say little about your contracts, tickets, or clinical notes. We grade and train on your corpus and your real questions.

You keep the capability

The trained models, test sets, rubrics, and scripts are yours at the end. You should not need us to find out whether your next release got worse.

A model that scores well on a benchmark and badly on your documents is not a good model. It is an unmeasured one.

Tell us where your AI falls short

Describe the system and the failure you are seeing. We will tell you what we would measure first, and whether we are the right people to help.

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