Give Your Systems the Ability to See, Read & Understand.
SourceMash's Computer Vision & NLP practice builds production AI systems that process images, video, documents, and natural language at scale — enabling machines to inspect products, read documents, understand conversations, monitor assets, and extract intelligence from unstructured content with accuracy and speed that no human team can match. From visual quality inspection on manufacturing lines to multilingual sentiment analysis across millions of customer interactions, we deliver perception and language intelligence directly into the workflows where your decisions are made.
80% of Enterprise Data Is Unstructured — and Untouched.
Images, video, documents, emails, call transcripts, product reviews, maintenance logs, and social media represent the vast majority of data your organisation generates every day — and almost none of it is being systematically analysed. Computer vision and NLP models are the tools that turn this dark data into operational intelligence: defects detected before products leave the factory floor, customer sentiment tracked across every channel in real time, contracts analysed in seconds instead of hours, and regulatory filings extracted automatically without analyst effort.
Every SourceMash CV and NLP system is built for production, not a research demo. That means edge-deployable models where latency matters, drift monitoring in production, retraining pipelines that keep models accurate as real-world distributions shift, and integration into the operational systems where insights need to arrive for decisions to actually change.
Edge & Cloud Deployment
Models optimised for edge inference (ONNX, TensorRT, OpenVINO) where latency or connectivity constraints exist — or cloud-deployed via REST API for centralised scaling.
Custom Training on Your Data
Foundation models fine-tuned on your specific products, documents, language, and domain — achieving production accuracy that off-the-shelf APIs cannot match for your specific use case.
Integrated Into Operational Workflows
Vision and NLP model outputs integrated into your MES, ERP, CRM, or CMMS — so insights arrive where decisions are made, not locked in a separate analytics tool.
Continuous Monitoring & Retraining
Data drift detection, prediction distribution monitoring, and automated retraining pipelines that keep models accurate as your products, processes, and language evolve over time.
Solution 01
Visual Quality Inspection & Defect Detection
Manual visual quality inspection is one of the most persistent bottlenecks in manufacturing, food processing, electronics assembly, pharmaceuticals, and logistics. Human inspectors are inconsistent (accuracy varies by shift, time of day, and fatigue), slow (inspection rates are constrained by human visual processing speed), and expensive (quality inspection headcount is a significant operating cost). More critically, even well-trained human inspectors miss 20-30% of defects on high-speed production lines where inspection time per unit is measured in fractions of a second.
SourceMash builds AI-powered visual inspection systems that process camera feeds at line speed — detecting surface defects, dimensional deviations, assembly errors, contamination, label misalignment, packaging damage, and other quality issues with accuracy that consistently exceeds human inspector performance, at throughput rates no manual team can match. Our systems integrate with your existing production line cameras or we specify and configure new vision hardware — and connect directly to your MES, quality management system, or reject mechanism for automated non-conforming unit handling.
Defect Types We Detect
Trained on your specific products and defect taxonomy — not generic defect categories
Surface Defects
Scratches, dents, cracks, chips, pitting, corrosion, discolouration, bubbles, and texture anomalies on metal, plastic, glass, ceramic, and composite surfaces — at micron-level resolution with structured light or standard RGB cameras.
Dimensional & Geometric
Dimensional deviation, warping, missing features, wrong hole placement, thread defects, and geometric non-conformance — using photogrammetric reconstruction, structured light scanning, or calibrated stereo vision with sub-millimetre precision.
Assembly Verification
Missing components, wrong component, incorrect orientation, improper fastening, solder defects (bridging, cold joints, tombstoning), and connector seating verification — on PCBs, mechanical assemblies, and packaged products.
Food & Pharma Quality
Foreign body detection, contamination, colour deviation, shape non-conformance, fill level verification, cap integrity, tablet coating defects, and blister pack completeness — with hygienic camera housing options for food-grade environments.
Label & Packaging Inspection
Label placement, print quality, barcode readability, expiry date legibility, batch code verification, packaging seal integrity, and carton damage detection — integrated with serialisation and traceability systems.
Weld & Joint Inspection
Weld bead geometry, porosity, undercut, spatter, incomplete fusion, and heat-affected zone anomalies — using thermal imaging, X-ray, or high-resolution optical cameras with specialist 3D reconstruction for critical structural applications.
From Camera Feed to Quality Decision — Five Stages
The end-to-end visual inspection pipeline — from image capture to MES integration
Image Acquisition
Triggered or continuous image capture from line-speed cameras — colour, monochrome, thermal, or multispectral — with hardware-synchronised illumination for consistent images at full production speed.
Preprocessing
Real-time preprocessing — background normalisation, distortion correction, region-of-interest cropping, and enhancement — to maximise defect signal-to-noise ratio before model inference.
Model Inference
Defect detection model inference on edge GPU or camera-integrated processor — classification, localisation, and severity scoring for each detected anomaly, in under 50ms per frame.
Decision & Reject
Pass/fail decision based on defect type, severity, and location rules — triggering reject mechanism, marking unit with defect data, and logging to quality management system in real time.
Analytics & MES
Defect trend dashboards, SPC-integrated quality metrics, and Pareto analysis by defect type, shift, and line — pushed to your MES for process improvement and SPC monitoring.
Vision Hardware We Specify & Integrate
We work hardware-agnostic — integrating with your existing cameras or specifying the right vision hardware for your inspection requirements, line speed, and environmental conditions.
Solution 02
Object Detection, Tracking & Video Analytics
Object detection and video analytics transform passive camera infrastructure into an active operational intelligence layer. Where traditional CCTV records what happens for post-hoc review, AI-powered video analytics processes camera feeds in real time — detecting, classifying, tracking, and alerting on specific objects, events, and behaviours as they occur. This enables automated safety compliance monitoring, retail footfall and shopper behaviour analytics, logistics dock and yard management, warehouse inventory visibility, smart city traffic management, and perimeter security — at the scale of an entire facility or camera network, without proportional growth in human monitoring headcount.
SourceMash builds object detection and tracking systems using state-of-the-art architectures (YOLOv10, RT-DETR, SAM 2) fine-tuned on your specific operational environment — handling the domain-specific appearance variation, lighting conditions, and object categories that off-the-shelf models from general-purpose APIs cannot reliably handle in production. We optimise for your deployment constraint — edge inference on existing NVR hardware, cloud processing of uploaded video, or hybrid architectures for large camera networks.
Video Analytics Use Cases
Industry-specific applications where real-time video intelligence creates measurable operational value
Workplace Safety & PPE Compliance
Real-time detection of PPE non-compliance — missing hard hats, high-vis vests, safety glasses, and gloves — with immediate alert to supervisors. Restricted zone intrusion detection, forklift proximity alerts, and ergonomic risk posture monitoring.
Retail Footfall & Shopper Analytics
Customer counting, dwell time analysis, heat mapping by store zone, queue length monitoring, conversion funnel analysis by fixture, and shelf interaction tracking — fully GDPR-compliant using anonymised silhouette detection, no facial recognition.
Warehouse & Logistics Intelligence
Dock door occupancy monitoring, trailer loading/unloading verification, pallet detection and counting, inventory location tracking via overhead camera networks, forklift path optimisation, and anomaly detection for misplaced or damaged goods.
Smart City & Traffic Analytics
Vehicle counting, classification, and speed measurement; pedestrian flow analysis; illegal parking detection; junction saturation monitoring; incident detection (stopped vehicles, wrong-way driving); and adaptive traffic signal optimisation from real-time flow data.
Perimeter Security & Intrusion Detection
AI-powered video analytics that distinguishes genuine security events (person in restricted zone, vehicle in perimeter, left object) from false triggers (animals, foliage movement, lighting changes) — dramatically reducing false alarm rates from traditional motion-detection systems.
Process & Equipment Monitoring
Visual monitoring of industrial processes — flame and smoke detection, liquid level monitoring, conveyor belt tracking, gauge reading, and equipment status assessment from camera feeds — supplementing or replacing sensor-based monitoring with camera-derived measurements.
Detection Architectures We Deploy
We select the right model architecture for your accuracy, latency, and deployment constraint
| Architecture | Speed (FPS) | Accuracy | Best For | Edge Deployable |
|---|---|---|---|---|
| YOLOv10 / YOLOv9 | 30 – 120 FPS | mAP 54 – 62% | Real-time edge inference | ✓ Yes |
| RT-DETR (Real-Time DETR) | 25 – 60 FPS | mAP 55 – 64% | High accuracy, real-time | ✓ Yes |
| SAM 2 (Segment Anything) | 5 – 15 FPS | Near-perfect segmentation | Instance segmentation | Partial |
| Detectron2 / Mask R-CNN | 5 – 20 FPS | mAP 40 – 55% | Complex instance segmentation | GPU required |
| CLIP / Vision Transformers | 2 – 10 FPS | Zero-shot generalisation | Open-vocabulary detection | Cloud preferred |
Solution 03
OCR & Advanced Document Intelligence
Optical character recognition has been a solved problem for structured, printed text for decades — but enterprise documents are rarely structured, uniformly formatted, or purely printed. Handwritten annotations, mixed-language content, complex table structures, degraded scans, stamped text over printed fields, multi-column layouts, and form fields with variable content are the norm in real document processing workflows. SourceMash builds document intelligence systems that go far beyond basic OCR — combining layout-aware deep learning models with LLM-based extraction to understand document structure, extract semantically meaningful data, and integrate results into downstream systems at production scale.
Our document intelligence stack is engineered for the specific document types and quality levels you actually process in production — not for clean, synthetic benchmarks. We fine-tune extraction models on samples of your real documents, measure field-level extraction accuracy rather than character-level OCR accuracy, and design exception workflows for the genuinely ambiguous cases that every real document corpus contains.
What Our Document Intelligence Stack Does
Beyond character recognition — a complete pipeline from raw image to structured, validated, integrated data
Layout Understanding
LayoutLM v3 and Donut models identify document structure — sections, headers, paragraphs, tables, form fields — preserving semantic relationships that character-level OCR cannot capture.
Complex Table Extraction
Multi-row header tables, merged cells, nested tables, and rotated table content extracted with full structural fidelity — including column header association and row-level data validation.
Handwriting Recognition
Printed and cursive handwritten text recognised using specialised HTR models fine-tuned on your handwriting corpus — with word-level confidence scores and flagging of low-confidence regions.
Stamp & Signature Detection
Detection and localisation of stamps, seals, signatures, and hand-annotations overlaid on printed content — with classification of stamp type, content extraction, and signature presence verification.
Multilingual & Mixed-Script
Documents containing multiple languages or scripts — Arabic/English, Hindi/English, CJK/Latin mixed content — processed correctly with script-aware segmentation and language-specific OCR models.
Cross-Field Validation
Extracted values validated against each other (totals match line items, dates are chronologically consistent, referenced entities match master data) — catching extraction errors that field-level confidence scores alone cannot identify.
Industries We Serve with Document Intelligence
Domain-specific extraction models trained for your document types and vocabulary
Solution 04
Sentiment Analysis & Text Analytics
Your customers are telling you exactly what they think about your products, services, and brand — in reviews, support tickets, social media posts, call centre transcripts, survey responses, and chat logs. The volume of this feedback is enormous and growing, and almost none of it is being systematically analysed in real time. SourceMash builds sentiment analysis and text analytics systems that process every customer interaction, review, and feedback signal at scale — classifying sentiment, identifying specific topics and themes, detecting emerging issues before they become crises, and surfacing voice-of-customer intelligence your product, marketing, and CX teams can actually act on.
Our sentiment models go far beyond positive/negative/neutral classification. We build aspect-based sentiment systems that identify sentiment at the level of specific product features, service attributes, and touchpoints — telling you not just that a review is negative, but that it is negative specifically about delivery speed and packaging quality while positive about product quality. We build custom sentiment taxonomies for your specific industry, product, and brand context rather than applying generic categories that miss the nuances that matter in your business.
Text Analytics Capabilities We Build
A layered stack of NLP capabilities that transform unstructured text into structured business intelligence
Aspect-Based Sentiment Analysis (ABSA)
Sentiment classified at the level of specific product attributes, service dimensions, and touchpoints — identifies which aspects customers praise or criticise and with what intensity, across any text channel at scale.
Topic Modelling & Theme Discovery
Unsupervised and semi-supervised topic models that identify emerging themes in large text corpora — surfacing new issues, trending complaints, and unexpected positive feedback patterns that keyword-based monitoring misses entirely.
Call Centre Transcript Analysis
Automated analysis of call recordings — transcription, speaker diarisation, sentiment trajectory, topic classification, escalation signal detection, agent performance scoring, and compliance phrase monitoring — at 100% call coverage.
Early Warning & Issue Detection
Real-time streaming sentiment monitoring across review platforms, social media, and support channels — statistically detecting volume spikes and sentiment shifts for specific products, topics, or geographic regions before they become visible in aggregated dashboards.
Customer Feedback Intelligence
Survey response, NPS verbatim, and online review analysis — automatic coding of open-ended responses, theme clustering, driver analysis linking sentiment to specific service attributes, and longitudinal trend tracking across survey waves.
Multilingual Sentiment Intelligence
Native multilingual models trained on your specific language mix — not translation-then-analysis pipelines that lose sentiment nuance. Covers 40+ languages with language-specific fine-tuning for regional expressions and domain-specific terminology.
Data Sources We Connect
Every channel where your customers and employees express opinions — processed together into a unified intelligence layer
Solution 05
Named Entity Recognition & Information Extraction
Information extraction transforms unstructured text — news articles, research papers, legal filings, clinical notes, financial reports, maintenance logs, and social media — into structured data that can be searched, analysed, aggregated, and acted upon systematically. Named entity recognition identifies and classifies the specific entities that appear in text (companies, people, locations, products, dates, monetary values, legal provisions, clinical terms, technical specifications) while relation extraction identifies the semantic relationships between them — enabling automated knowledge graph construction, contract clause population, adverse event detection, competitive intelligence gathering, and regulatory compliance monitoring at scale.
Generic pre-trained NER models recognise standard entity types but miss the domain-specific entities that actually matter — specific chemical compound names, proprietary product identifiers, regulatory clause references, clinical measurement types, or operational procedure codes. SourceMash fine-tunes NER and relation extraction models on annotated samples from your specific corpus, achieving production accuracy on your entity types that generic models cannot approach.
Domain-Specific NER Applications
Fine-tuned for the entity vocabulary and text style of each specific domain
Clinical NLP & Medical NER
Extraction of symptoms, diagnoses, medications (with dosage and frequency), procedures, anatomical locations, lab values, and clinical measurements from EHR notes and discharge summaries — mapped to SNOMED CT, ICD-10, and RxNorm terminologies.
Legal Entity & Clause Extraction
Party names, effective dates, obligation clauses, termination triggers, penalty provisions, IP assignment terms, and governing law extracted from contracts — enabling contract lifecycle management, obligation monitoring, and risk clause flagging.
Financial NLP & Event Extraction
Company names, financial metrics, M&A events, earnings guidance, credit rating changes, and regulatory actions extracted from earnings calls, analyst reports, news, and SEC/regulatory filings — for investment intelligence and compliance monitoring.
Scientific & Patent NLP
Chemical compounds, biological entities, material properties, experimental methods, and patent claim terms extracted from scientific literature and patent filings — enabling competitive intelligence, prior art analysis, and R&D knowledge graph construction.
Maintenance & Operational Log NLP
Equipment identifiers, fault codes, maintenance actions, part numbers, and operational events extracted from free-text maintenance logs, work orders, and operator notes — enabling failure pattern analysis and maintenance history search.
News & Media Intelligence
Entities, events, and relationships extracted from news feeds across 40+ languages — for brand monitoring, supply chain risk intelligence, geopolitical risk tracking, competitive intelligence, and ESG controversy detection across global media sources.
From Raw Text to Knowledge Graph — Four Stages
Our information extraction pipeline — transforming unstructured text corpora into queryable, connected knowledge
Corpus Ingestion
Documents ingested from your data sources — file systems, databases, APIs, email archives, web scraping — with format normalisation, language detection, and preprocessing for downstream NLP.
NER & Coreference
Entity recognition identifies and classifies named entities throughout each document — with coreference resolution linking pronouns and aliases to their canonical entity references across document sections.
Relation Extraction
Semantic relationships between identified entities extracted — company-product relationships, person-organisation affiliations, event-entity associations, and domain-specific relations from your ontology.
Knowledge Graph & Search
Extracted entities and relations loaded into a knowledge graph (Neo4j, Amazon Neptune) or vector database — enabling semantic search, entity-centric querying, and relationship traversal.
Solution 06
Multimodal AI & Vision-Language Models
The most powerful and highest-value AI applications increasingly require understanding across multiple modalities simultaneously — images with text annotations, documents with charts, video with speech, products with specification documents, satellite imagery with geospatial metadata. Multimodal AI models that process and reason across visual, textual, and structured data inputs together unlock capabilities that single-modality approaches cannot achieve: automatically generating structured product catalogues from product images and existing description text, answering natural language questions about engineering drawings, detecting regulatory compliance in both document text and photographic evidence, and grounding LLM reasoning in specific visual evidence.
SourceMash's multimodal AI practice builds systems using vision-language foundation models (Claude Vision, GPT-4V, LLaVA, InternVL) and grounds them in your specific domain — your products, your documents, your operational context — through fine-tuning, retrieval augmentation, and structured tool use that connects model reasoning to your live data systems. The result is AI systems that understand your business context the way a knowledgeable human expert would, but at machine speed and scale.
Multimodal AI Applications We Build
Use cases where combining visual and language understanding creates capabilities impossible with single-modality AI
Visual Product Catalogue Generation
Automated generation of structured product descriptions, attribute extraction, and catalogue data from product images — combining visual recognition with existing data to produce consistent, SEO-optimised, multilingual product content at scale without manual copywriting effort.
Visual Question Answering (VQA)
Systems that answer natural language questions about specific images — "Is the weld bead width within specification?", "What is the expiry date on this label?", "Is this an acceptable component orientation?" — combining visual evidence with domain knowledge for inspection and verification tasks.
Engineering Drawing Intelligence
Natural language querying of engineering drawings, P&IDs, and schematics — extracting component lists, tolerance specifications, material callouts, and revision histories from CAD drawings and technical documents without manual interpretation by engineers.
Satellite & Aerial Image Intelligence
Change detection, land use classification, infrastructure mapping, crop health monitoring, and construction progress tracking from satellite and drone imagery — integrated with geospatial metadata and GIS systems for operational decision support.
Medical Image + Clinical NLP
Combined analysis of radiological images (X-ray, CT, MRI) with clinical text — cross-referencing visual findings with patient history, symptom narrative, and clinical notes to produce integrated diagnostic support outputs that contextualise image findings within the clinical story.
Insurance Damage Assessment
Combined analysis of damage photographs with policy documents and repair estimate texts — automated damage severity scoring, coverage eligibility assessment, reserve estimation, and fraud signal detection from the combined evidence of images and associated documentation.
Foundation Models We Fine-Tune for Your Domain
We select and fine-tune the right vision-language model for your accuracy, latency, and data privacy requirements — from open-weight models deployable entirely within your infrastructure to API-based foundation models augmented with retrieval and domain grounding.
Computer Vision & NLP Technology Stack
We select the right combination of vision backbone, NLP model, training framework, and deployment runtime for each project — optimising for accuracy, inference latency, data privacy, and edge or cloud deployment target.
What Our Clients Say
We had been living with a 2.3% customer escape rate on our PCB assemblies — costing us ₹4.2 crore annually in warranty claims and field returns. The visual inspection AI SourceMash deployed on our SMT lines now catches 99.6% of defects at line speed. False positive rate is below 0.4%, so the production team trusts it. Customer escapes are down 94% in six months. ROI was under four months.
We sell across 14 markets in 12 languages. Our review volume was 2 million per month and we were manually sampling 0.1% of them. The aspect-based sentiment system SourceMash built processes every review in real time, tells us exactly which product attributes are driving negative sentiment in which markets, and alerts our CX team to emerging issues within hours — not after they have gone viral. It has genuinely changed how we make product decisions.
Our contract review team was processing 50,000 contracts a year and spending 3-4 hours on each. The NLP extraction system SourceMash built pulls every material clause, flags non-standard deviations, and pre-populates our CLM system in minutes. Our lawyers now review what the AI extracted and focus on the genuinely complex judgement calls. Review time is down 75% and our coverage went from sampling to 100% of contracts.
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Perspectives, research, and practical guidance from our enterprise technology experts.
