Vision and language systems that handle the messy real world
Production computer-vision and NLP systems — from OCR and document extraction to classification, detection, and multi-modal search — built for the real world's edge cases.
Perception that survives edge cases
The models make headlines, but production reliability comes from data, evaluation, and honest error handling.
Object detection & tracking
Detectors and trackers tuned for your imagery — retail shelves, factory lines, warehouse cameras, or drone feeds.
OCR & document AI
Text extraction that respects layout — tables, key-value pairs, and multi-page documents — with confidence per field.
Text classification
Intent, sentiment, topic, and priority labels applied at inbox scale — with active-learning loops to close the tail.
Named-entity extraction
Structured fields from unstructured text — parties, dates, amounts, clauses, and product codes — with reviewable output.
Multimodal search
Image, text, and metadata indexed together so a shopper, a lawyer, or a librarian can find the right item in one query.
Model evaluation
Per-class precision, recall, and slice-based error analysis so you know exactly where the model is weak.
From raw data to a monitored model
A four-phase engagement that spends real time on data before it spends real time on models.
Data audit
We inspect the data you have, the data you're missing, and the labeling quality of what already exists. No modeling until the data is honest.
Annotation & pipeline
A labeling guideline written for humans, an annotation platform your team can use, and a pipeline that turns new data into training sets automatically.
Model training
Baselines first, then iterations with error analysis between each. We optimize for the metric that matters to your operators, not to a leaderboard.
Deployment & drift monitoring
Batch, real-time, or edge deployment with input distribution and confidence monitoring — so a shifting world doesn't silently degrade quality.
Vision and language toolchain
Frameworks, model families, and serving infrastructure we use across CV and NLP projects.
Where perception pays back
Tasks with high volume, repeatable inputs, and clear success criteria — where machines earn the reviewer's time back.
Document extraction pipelines
Invoices, forms, and statements turned into structured data with per-field confidence and a reviewer UI for low-confidence rows.
Retail shelf analytics
Share-of-shelf, planogram compliance, and out-of-stock detection from store photos or overhead cameras.
Manufacturing defect detection
Inline vision inspection with tuned recall for critical defects and clear tolerances for the borderline cases.
Content moderation
Multi-signal moderation — images, text, and metadata — combined with policy-aware thresholds and human review queues.
Contract clause tagging
Identify and structure clauses — indemnity, termination, IP — across contract portfolios for review and search.
Support ticket routing
Classify inbound tickets by product, intent, and urgency; route to the right queue with the fields already parsed out.
What honest perception delivers
Concrete outcomes for operations, product, and compliance teams once a working model is in the loop.
Automate manual review
Route confident cases straight through and reserve human review for genuinely ambiguous ones — measurably.
Improve throughput
Handle peak volumes without linearly scaling headcount, with predictable per-item latency at load.
Reduce error rate
Consistent labeling and enforcement across shifts and geographies, with a live view of where errors still occur.
Handle real-world edge cases
A model that survives dirty scans, angled photos, mixed languages, and the surprises production always brings.
Perception in a consumer product
FitPulse combines on-device signals, wearable data, and adaptive coaching in a shipping mobile experience.
Mobile • Health
Why teams choose us for vision and NLP
Applied ML that spends its time on the boring, decisive work — data quality, evaluation, and deployment.
Learn more about us →Data-first practice
We invest in labeling guidelines, annotator training, and slice-based evaluation before we touch model architecture.
Deployment reality
We pick batch, real-time, or edge for real reasons — latency, bandwidth, privacy — and design the pipeline around that choice.
Honest metrics
We report per-class and per-slice metrics with confidence intervals. No cherry-picked averages hiding a weak class.
Drift you can see
Input distribution and confidence monitoring wired in from the first release, so degradation shows up on a dashboard, not in a complaint.
Vision & NLP questions we get
What operations, security, and product leaders ask before they commit to a perception system.
How much labeling do we really need?
Can this run on-device or at the edge?
What accuracy should we expect?
How do you handle sensitive imagery or documents?
How often will the model need to be retrained?
On-device or cloud — how do we choose?
Put a perception model into the workflow that needs it.
Share the task and a sample of the data. We'll come back with a data audit, a baseline plan, and a target evaluation metric.
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