Most tools work on clean inputs.
We built ours for everything else.
The standard stack — OCR platforms, RAG frameworks, workflow builders — covers the happy path well. It degrades badly on handwritten fields, degraded scans, domain-specific terminology, untested pipelines, and retrieval that needs precision over similarity.
We hit those walls on real client work. Each tool below is a direct response to a specific problem we couldn't solve with what existed. The technical choices aren't arbitrary — they're documented, and we'll explain them if you ask.
Document Extraction
OCR that knows when it's uncertain.
The hard problem in LLM-based document extraction isn't accuracy on clean inputs, any decent model handles those. It's that LLMs fail silently. A hallucinated value looks identical to a correct one. You can't prompt your way out of this; the model has no reliable access to its own uncertainty.
Echo reads the model's output probability distribution directly to score confidence at the field level. Fields the model committed to flow automatically. Fields it hesitated on are flagged with their source context, so whatever sits downstream can handle them appropriately.
Full audit trail either way.
Built on google/gemini-3.1-flash via OpenRouter (configurable). Structured JSON output via prefill constraints. Confidence scoring via logprob distribution on value tokens only — not the full sequence.
Automation that's tested before it touches your data.
Agentic pipelines break in predictable ways — edge cases the builder didn't anticipate, upstream changes, silent failures that produce bad output without raising an error. You usually find out when something downstream is already wrong.
Passband runs every pipeline through a structured test battery before it goes into production: normal inputs, malformed inputs, edge cases, and failure scenarios. Each run defines what acceptable behaviour looks like under real conditions.
Pipelines that pass ship. Pipelines that don't tell you exactly where they failed.
Test cases are version-controlled alongside the pipeline. Regression testing runs automatically on any update. Failures are loud by design.
Workflow QA
Operational Knowledge
Retrieval that finds the right document. Not just the closest one.
Standard retrieval finds documents that are semantically close to your query. That's fine for general knowledge. It breaks down when the right answer depends on a specific term, a precise code, or a technical label — things a general model treats as roughly equivalent to their synonyms.
Resonance is built around keywords, not document similarity. We extract the terms that actually matter from each document at indexing time, weight them by importance, and match against those — not the raw text. Queries go through the same decomposition before retrieval runs.
The result is retrieval that respects precision, not just proximity.
Two-prompt architecture: keyword extraction at indexing, keyword decomposition at query time. Embeddings on keyword representations, not full documents. Similarity scoring weighted by keyword importance. Metadata filtering as pre-retrieval scope layer.
A general model doesn't know your terminology. A fine-tuned one does.
Fine-tuning is frequently oversold. Prompt engineering or retrieval solves most performance problems and is cheaper to maintain. We'll tell you which applies before recommending fine-tuning.
When it is the right call, the differentiator isn't the training — it's the data. Most fine-tuning failures are data failures. Overdrive is built around that: structured dataset management, documented methodology, reproducible runs from base model selection through staged production release.
Torchtune for text-only fine-tuning (transparency, full pipeline visibility). Axolotl for vision LoRA (Qwen3.5 9B). Storage on Cloudflare R2. GPU via RunPod or Modal. Experiment tracking in structured JSONL before heavier tooling.
Fine-Tuning
Want to see one of these in action?
Passband and Echo are in active use on client work. If you want a live demo or a technical conversation, that's what the free diagnostic is for.