AI case studies that show you exactly how production AI gets built.

Site progress gets verified against the BIM model, not against a supervisor’s notebook. 360° walkthrough captures are reconstructed in 3D and aligned to BIM at GUID level, so every element carries a progress state and a deviation flag.

• 40–60% reduction in supervision effort
• Deviation flags weeks before billing disputes
• Floor-wise dashboards and PDF engineering reports, rolled out across multiple sites with reduced rework

Scope: Insta360 capture, SLAM, SfM reconstruction, GUID-level BIM alignment, reporting pipeline.

Contact-wire height and stagger measured continuously from a vehicle moving at 90 km/h. Stereo vision with depth fusion runs 2K at 60 FPS, with tolerance alerts and CSV/Excel reports out.

• 10–20x inspection coverage vs walking patrols
• 50% targeted pantograph wear reduction
• Manual walking inspections eliminated, no linemen on masts

Scope: YOLOv11, ZED stereo, CUDA, GPS tagging.

The drawing itself gives up the plot dimensions. Detection and segmentation locate plan components, OCR reads dimensions and scale markers, and pixel-to-metre conversion computes the rest.

• Boundary lengths, plot area, and setback distances in seconds
• Owner’s plot auto-highlighted with overlays

Scope: YOLO detection and segmentation, OCR, geometry engine.

Enforcement happens at the intersection, in real time. Edge units detect U-turn violations, helmet and seatbelt compliance, and read number plates, feeding a control-room dashboard.

• Reduced enforcement manpower
• Citywide edge-deployable architecture

Scope: Jetson, YOLOv3, DeepStream, Qt C++.

Built after open-source tools kept overwriting verified boxes on partially labeled railway datasets. This desktop editor updates only the modified lines in YOLO .txt files and leaves everything else untouched.

• 30–40% faster QA
• 20,000+ images corrected from 10+ inspection videos
• Higher-precision training data

Scope: PyQt5, Python, YOLO label format.

Compliance is the default state of the imaging pipeline. DICOM metadata is de-identified automatically, organs are segmented by AI, and results convert to standards-compliant DICOM-SEG.

• PHI stripped without manual passes
• Reduced annotation effort
• Interoperable outputs for downstream systems

Scope: MONAI, Python, DICOM-SEG conversion.

Point an RTSP stream at it and deploy a vision model in days. No-code, multi-tenant CV deployment: drag-and-drop setup, VLM-assisted dataset annotation, model versioning with accuracy tracking, event-based video storage.

• Months of deployment reduced to days
• 50–70% annotation cost reduction
• Automated safety-compliance monitoring with real-time alerts, AI-vendor dependency eliminated

Scope: Kafka, YOLO, DeepStream, Go.

Photograph the part and the system returns the SKU. Embedding extraction plus FAISS retrieval ranks visually similar spare parts across thousands of near-identical SKUs, with category prediction on top.

• Catalog hunting reduced to seconds
• Reduced maintenance downtime
• Less dependency on veteran experts

Scope: EfficientNet-B0, FAISS, PyTorch.

The cameras record only when the 3D scene actually changes. Depth-frame comparison detects volumetric change and triggers synchronized RGB and depth streaming on motion alone.

• 50–80% storage cut
• Optimized bandwidth
• Event-based capture

Scope: RealSense, Ouster LiDAR, stereo rigs.

Full object detection on a battery-powered device, no cloud round trip. YOLO is optimized through the SNPE SDK with a C++ capture pipeline for fully on-device inference.

• Zero cloud dependency
• Significantly reduced power consumption
• Scalable edge deployment

Scope: QCS610, SNPE SDK, C++, XTensor.

Detection runs at the silicon level. Memory-optimized camera capture feeds YOLO on an NPU with low-latency rendering.

• Real-time performance on-device
• Lower cloud cost and power draw
• Faster time-to-market

Scope: YOLO, NPU toolchain, C, Python.

Raw film in, box-score analytics out, at a scale of 600k games a year. Detection, tracking, and event recognition cover players, ball, shots, rebounds, and assists, with annotated overlays and CSV stats.

• 85%+ F1 event accuracy
• Under 6 hours per game
• PyTorch converted to TensorRT inside DeepStream, Dockerized, Kubernetes-orchestrated on AWS GPU nodes for parallel processing

Scope: RF-DETR, ByteTrack, TensorRT, Kubernetes.

Blurry legacy archives come back at 4K. The pipeline runs 2x and 4x upscaling with noise removal, face restoration, and tiling for memory efficiency.

• 4K-ready output in seconds
• Legacy content monetization enabled

Scope: Real-ESRGAN, GFPGAN, FFmpeg.

Shoulder measurements from a phone camera. Pose estimation with face-based scale calibration converts pixels to centimetres and triggers size recommendations on standard consumer devices.

• Fewer returns
• Improved checkout confidence with no special hardware

Scope: MediaPipe Pose, OpenCV, NumPy.

Business users ask in plain English and the warehouse answers in seconds. Chat converts questions into SQL against the bank’s Oracle warehouse: vector-search schema matching, automated error-correction retries, summarized results with suggested follow-ups.

• 1-day report turnaround reduced to seconds
• Open-source models keep costs controlled in a regulated environment
• Zero SQL needed for business users

Scope: LangChain, Ollama, vector DB, Oracle.

Feed it winning ads and it hands back a full creative engine. The system analyzes high-performing ad boards through frame extraction, transcription, and visual analysis, then generates scripts, product and UGC-style visuals, AI voiceovers, and short-form video variants in bulk.

• Production weeks reduced to hours
• 90% less manual creative work
• Dozens of variations in minutes for A/B testing

Scope: GPT-5, Fal.ai, Veo, ElevenLabs.

Describe the building in plain text and get a CAD-ready floor plan back in seconds. NLP extracts rooms and spatial relationships; a generation agent drafts the layout while a validation agent checks overlaps, placement logic, and Vastu compliance.

• Hours of drafting reduced to seconds
• Direct AutoCAD DXF export
• Unlimited layout variations per brief

Scope: Gemini, LangChain, geometry engine, ezdxf.

Every meal plan carries a gastroenterologist’s protocols. 13 integrated modules span meal planning, recipe generation, grocery building, barcode scanning, restaurant menu analysis, probiotic evaluation, colonoscopy-prep scheduling, and a diet chatbot.

• Covers 15–20 disorders
• Every response filtered through condition, doctor-approved diet, preferences, and allergies
• SaaS-ready clinic deployment

Scope: GPT-4o mini, Gemini, Flutter.

From prescription to plate to shopping list in a single workflow. Recommends dishes matching prescribed diet types, applies rule-based safety filtering, and explains each choice in plain language.

• Pantry-aware grocery lists
• Extensible across cuisines and constraints

Scope: Gemini API, Python, Streamlit.

Market chatter becomes a confidence-scored signal. The system scrapes and transcribes news, social, and YouTube; sentiment plus RSI and MACD indicators feed a custom fine-tuned quantized LLM.

• Minutes of analysis reduced to seconds
• Confidence-weighted signals on a live dashboard
• Scales across tickers

Scope: FinBERT, Faster-Whisper, FastAPI.

Seven pipelines run one client’s entire content operation: RAG email auto-replies in under 60 seconds, trend-researched multi-platform posting, avatar video production, product-image editing, product video generation, a 6-platform virality scraper, and AI viral playbooks.

• 40+ hours per week of manual work replaced
• Every action gated by Telegram human approvals
• Modular, client-owned infrastructure

Scope: n8n, Qdrant, HeyGen, LLaMA 3.3.

A raw idea returns with market data attached. LLM refinement and domain classification feed automated market-data extraction and competitor analysis with visual dashboards.

• 70% faster validation
• Data-driven go/no-go decisions

Scope: LLMs, SerpAPI, Plotly, Python.

New arXiv papers become scheduled LinkedIn posts. The pipeline scrapes and parses new research, summarizes it, and publishes on a fixed schedule.

• 3 posts per week, fully automatic
• Consistent thought-leadership cadence with zero drafting effort

Scope: Gemini 2.0 Flash, APScheduler.

A text script goes in and an instructor-led video comes out. Script parsing, voice synthesis, avatar generation, and automated rendering run as one pipeline.

• 80–90% cost cut
• Minutes per video instead of days
• Unlimited scaling with no studio or crew

Scope: Synthesia, ElevenLabs, Python.

Catalog-scale jewelry photography without a single photoshoot. The pipeline places jewelry on generated or provided models, detecting ears, neck, and wrist for precise placement while preserving exact product shape, texture, and clarity under realistic lighting.

• Days of production reduced to minutes
• Photoshoot costs removed
• Consistent presentation across large catalogs

Scope: Diffusion models, image-to-image pipelines.

Your face in any fantasy scene, quality-checked before delivery. Face detection and embedding extraction condition SDXL via IP-Adapter, and SSIM-based automated quality filtering runs before anything ships.

• Near-zero manual editing
• GPU-scalable architecture

Scope: SDXL, InsightFace, IP-Adapter.

A photoreal face that answers in real time. Diffusion-based motion synthesis is driven by audio features, with idle-animation management and realtime dialogue behind it.

• Real-time latency
• Hyper-realistic engagement compared with text chatbots

Scope: LIA, diffusion, GPT-4o Realtime.

Upload a paper, get systematic-review-grade analysis back in one pass. The pipeline extracts text, tables, and figures, identifies clinical outcomes behind reviewer approval gates, and pulls effect sizes, CIs, and p-values.

• Hours per paper reduced to one automated pass
• RoB2 bias assessment and GRADE tables, including SWiM
• Batch ZIP processing with cross-paper querying via knowledge graph and RAG chatbot. Phase 2 adds multi-reviewer workflows, COI, PRISMA 2020

Scope: LLMs, Neo4j, RAG, Excel automation.

A full tender package becomes one queryable source before the bid deadline. The system ingests every document, drawings included, and maps clauses, requirements, and risks into a knowledge graph.

• Weeks of manual review reduced to hours
• Missing mandatory documents flagged automatically
• Risky terms surfaced early: LDs, liability caps, termination clauses

Scope: Neo4j, LLMs, OCR, FastAPI. Parallel processing, no expert bottleneck.

The drawing itself gives up the plot dimensions. Detection and segmentation locate plan components, OCR reads dimensions and scale markers, and pixel-to-metre conversion computes the rest.

• Boundary lengths, plot area, and setback distances in seconds
• Owner’s plot auto-highlighted with overlays

Scope: YOLO detection and segmentation, OCR, geometry engine.

25,000+ public tenders classified in a single automated pass. Two workflows: LLM classification with enforced JSON output, or a supervised model for bulk runs. Each tender gets Industry, Sub-Industry, and Works/Service columns appended.

• Weeks of manual tagging reduced to 2–3 minutes
• Zero infrastructure cost
• Retrainable as categories evolve

Scope: Gemini API, Word2Vec, XGBoost.

Beam schedules stop being a counting exercise. Regex plus spatial proximity matching counts beams on structural drawings and associates each with its length, handling orientation logic along the way.

• Hours of manual counting reduced to seconds
• Annotated audit PDF as a verification trail
• Counting errors eliminated

Scope: PyMuPDF, Python, Gradio.

The protocol PDF goes in, study-ready eCRF files come out. The system parses trial protocols, extracts demographics, age limits, vitals, and inclusion/exclusion criteria, standardizes the terminology, and fills predefined eCRF Excel templates.

• Study-ready eCRFs generated automatically
• Chatbot over the extracted data
• Reusable across studies

Scope: LangChain, OpenAI, Ollama, Neo4j.

The report is summarized before the doctor opens the file. Lab reports and radiology summaries are converted into standardized, structured summaries delivered on a clinician dashboard.

• 60–80% less review time
• Increased patient throughput
• Consistent summaries across clinicians

Scope: DocTR, LangChain, LangGraph, FastAPI.

Any supplier’s invoice layout lands as GST-ready Excel. The pipeline reads 40+ fields per line item; TrOCR handles complex and merged-cell tables while a regex layer validates dates, tax fields, and batch codes.

• Hours per day reduced to about a minute
• One row per item per invoice
• Audit-ready output across supplier formats

Scope: DocTR, TrOCR, regex validation layer.

Three platforms, 108 sheets, one reconciliation run. The system logs into Glovo, Just Eat, and Uber Eats, downloads every invoice and CSV per billing period, and extracts commissions, payouts, and taxes from the PDFs.

• Account-level master sheet plus per-store analysis with formulas and pivots
• Duplicate and missing-invoice risk eliminated

Scope: Playwright, Bright Data, S3, Sheets.

Misapplied laws get flagged line by line, in three languages. The system parses text and scanned contracts, matches them against a common-mistakes database, and maps clauses to known laws to flag false or misapplied references.

• Hours of legal review reduced to minutes
• Line and page precision with suggested corrections
• Spanish, Catalan, and English at volume

Scope: LLMs, OCR, NLP, annotation UI.

The resume pile becomes a queryable knowledge graph. Resume parsing with OCR fallback, semantic job-description matching, graph modeling, and conversational queries over the whole candidate pool.

• 90% screening cut
• Semantic ranking and faster hiring cycles

Scope: Gemini 2.0, Neo4j, LangChain, FastAPI.

Upload a paper, get systematic-review-grade analysis back in one pass. The pipeline extracts text, tables, and figures, identifies clinical outcomes behind reviewer approval gates, and pulls effect sizes, CIs, and p-values.

• Hours per paper reduced to one automated pass
• RoB2 bias assessment and GRADE tables, including SWiM
• Batch ZIP processing with cross-paper querying via knowledge graph and RAG chatbot. Phase 2 adds multi-reviewer workflows, COI, PRISMA 2020

Scope: LLMs, Neo4j, RAG, Excel automation.

Business users ask in plain English and the warehouse answers in seconds. Chat converts questions into SQL against the bank’s Oracle warehouse: vector-search schema matching, automated error-correction retries, summarized results with suggested follow-ups.

• 1-day report turnaround reduced to seconds
• Open-source models keep costs controlled in a regulated environment
• Zero SQL needed for business users

Scope: LangChain, Ollama, vector DB, Oracle.

A full tender package becomes one queryable source before the bid deadline. The system ingests every document, drawings included, and maps clauses, requirements, and risks into a knowledge graph.

• Weeks of manual review reduced to hours
• Missing mandatory documents flagged automatically
• Risky terms surfaced early: LDs, liability caps, termination clauses

Scope: Neo4j, LLMs, OCR, FastAPI. Parallel processing, no expert bottleneck.

Seven pipelines run one client’s entire content operation: RAG email auto-replies in under 60 seconds, trend-researched multi-platform posting, avatar video production, product-image editing, product video generation, a 6-platform virality scraper, and AI viral playbooks.

• 40+ hours per week of manual work replaced
• Every action gated by Telegram human approvals
• Modular, client-owned infrastructure

Scope: n8n, Qdrant, HeyGen, LLaMA 3.3.

The resume pile becomes a queryable knowledge graph. Resume parsing with OCR fallback, semantic job-description matching, graph modeling, and conversational queries over the whole candidate pool.

• 90% screening cut
• Semantic ranking and faster hiring cycles

Scope: Gemini 2.0, Neo4j, LangChain, FastAPI.

Ask the farm data which fields are at risk this week. Chat runs over a relational store plus a relationship graph covering field, crop, insect, fertilizer, and irrigation, generating queries dynamically, detecting trends, and visualizing relationships with suggested follow-ups.

• Earlier pest interventions
• Direct access for non-technical agronomists

Scope: LangChain, PostgreSQL, Neo4j.

2,500 refund disputes a week, filed on platforms that offer no API. Four agents share the work: a Navigator, a Form Filler, a vision Validator, and Recovery. They read weekly refund rows from Sheets, submit disputes on Uber Eats, Just Eat, and Glovo with human-like behavior, and write approval status back every Friday.

• 93–97% success rate
• 1,000–2,500+ disputes per week with zero added staffing
• Under 3–4 minutes per order, down from hours, with manual intervention under 5%

Scope: Playwright, LangGraph, GPT-5.

Describe the building in plain text and get a CAD-ready floor plan back in seconds. NLP extracts rooms and spatial relationships; a generation agent drafts the layout while a validation agent checks overlaps, placement logic, and Vastu compliance.

• Hours of drafting reduced to seconds
• Direct AutoCAD DXF export
• Unlimited layout variations per brief

Scope: Gemini, LangChain, geometry engine, ezdxf.

One AI layer watches email, profiles, and links at once. An agent architecture coordinates an email fraud classifier, social-profile behavioral analysis, and URL, domain, and SSL forensics, including links embedded inside images.

• Three fraud channels covered by one system
• Reduced manual investigation
• Modular integration into the existing security stack

Scope: RoBERTa, CrewAI, Graph API.

Month-end journals close in under a minute. NL-to-SQL over BigQuery drives automated finance reports and QuickBooks-ready journal creation across brands.

• 95% workload cut
• Reconciliation errors eliminated
• Multi-brand scaling

Scope: GPT-4.1, BigQuery, n8n, AWS S3.

Three platforms, 108 sheets, one reconciliation run. The system logs into Glovo, Just Eat, and Uber Eats, downloads every invoice and CSV per billing period, and extracts commissions, payouts, and taxes from the PDFs.

• Account-level master sheet plus per-store analysis with formulas and pivots
• Duplicate and missing-invoice risk eliminated

Scope: Playwright, Bright Data, S3, Sheets.

Seven pipelines run one client’s entire content operation: RAG email auto-replies in under 60 seconds, trend-researched multi-platform posting, avatar video production, product-image editing, product video generation, a 6-platform virality scraper, and AI viral playbooks.

• 40+ hours per week of manual work replaced
• Every action gated by Telegram human approvals
• Modular, client-owned infrastructure

Scope: n8n, Qdrant, HeyGen, LLaMA 3.3.

Thirty touchpoints, and the sequence stops the moment they reply. An AI gatekeeper qualifies inbound email, routes new vs existing contacts via CRM lookup, drafts personalized outreach, and escalates tone across 30 business-day-aware stages.

• 10–15 hours per week saved
• 100% lead capture coverage
• Zero over-follow-up, since reply detection halts the sequence

Scope: GPT-4.1, n8n, HighLevel, Sheets.

A keyword goes in and a finished research sheet comes out. The workflow watches the task platform via webhooks, pulls keyword intelligence, and generates a templated sheet computing revenue, profit, units, ROAS, and traffic tier, then writes back and emails the CSV.

• Hours of research reduced to minutes
• Research scales without headcount

Scope: SEMrush API, Sheets, webhooks.

Contact-wire height and stagger measured continuously from a vehicle moving at 90 km/h. Stereo vision with depth fusion runs 2K at 60 FPS, with tolerance alerts and CSV/Excel reports out.

• 10–20x inspection coverage vs walking patrols
• 50% targeted pantograph wear reduction
• Manual walking inspections eliminated, no linemen on masts

Scope: YOLOv11, ZED stereo, CUDA, GPS tagging.

Enforcement happens at the intersection, in real time. Edge units detect U-turn violations, helmet and seatbelt compliance, and read number plates, feeding a control-room dashboard.

• Reduced enforcement manpower
• Citywide edge-deployable architecture

Scope: Jetson, YOLOv3, DeepStream, Qt C++.

The cameras record only when the 3D scene actually changes. Depth-frame comparison detects volumetric change and triggers synchronized RGB and depth streaming on motion alone.

• 50–80% storage cut
• Optimized bandwidth
• Event-based capture

Scope: RealSense, Ouster LiDAR, stereo rigs.

Full object detection on a battery-powered device, no cloud round trip. YOLO is optimized through the SNPE SDK with a C++ capture pipeline for fully on-device inference.

• Zero cloud dependency
• Significantly reduced power consumption
• Scalable edge deployment

Scope: QCS610, SNPE SDK, C++, XTensor.

Detection runs at the silicon level. Memory-optimized camera capture feeds YOLO on an NPU with low-latency rendering.

• Real-time performance on-device
• Lower cloud cost and power draw
• Faster time-to-market

Scope: YOLO, NPU toolchain, C, Python.

Market chatter becomes a confidence-scored signal. The system scrapes and transcribes news, social, and YouTube; sentiment plus RSI and MACD indicators feed a custom fine-tuned quantized LLM.

• Minutes of analysis reduced to seconds
• Confidence-weighted signals on a live dashboard
• Scales across tickers

Scope: FinBERT, Faster-Whisper, FastAPI.

Who said what, who decided what, who owes what: available the moment the meeting ends. Speaker diarization, noise removal, and ASR with timestamp alignment produce structured AI summaries and SRT subtitles.

• Manual minute-taking eliminated
• Instant decision tracking
• Accessibility compliance

Scope: Faster-Whisper, NeMo, Demucs.

A voice tutor that corrects Australian vowels as you speak. Bi-directional voice conversations coach the FACE, GOAT, and TRAP vowels, with automated grammar, vocabulary, and pronunciation scoring plus session history analytics.

• Millisecond feedback
• Human-tutor dependency removed for practice

Scope: GPT-4o Realtime, WebSockets, Flutter.

A photoreal face that answers in real time. Diffusion-based motion synthesis is driven by audio features, with idle-animation management and realtime dialogue behind it.

• Real-time latency
• Hyper-realistic engagement compared with text chatbots

Scope: LIA, diffusion, GPT-4o Realtime.

Point an RTSP stream at it and deploy a vision model in days. No-code, multi-tenant CV deployment: drag-and-drop setup, VLM-assisted dataset annotation, model versioning with accuracy tracking, event-based video storage.

• Months of deployment reduced to days
• 50–70% annotation cost reduction
• Automated safety-compliance monitoring with real-time alerts, AI-vendor dependency eliminated

Scope: Kafka, YOLO, DeepStream, Go.

Raw film in, box-score analytics out, at a scale of 600k games a year. Detection, tracking, and event recognition cover players, ball, shots, rebounds, and assists, with annotated overlays and CSV stats.

• 85%+ F1 event accuracy
• Under 6 hours per game
• PyTorch converted to TensorRT inside DeepStream, Dockerized, Kubernetes-orchestrated on AWS GPU nodes for parallel processing

Scope: RF-DETR, ByteTrack, TensorRT, Kubernetes.