Inspection Coverage, No sampling — every unit inspected at line speed
Real-time Inference. YOLO-based detection for live-feed scenarios
Build Timeline. From data access to production deployment
Security Standard, SOC 2, GDPR, HIPAA & AMLD compliant builds
Computer vision at enterprise scale means integrating visual inference into the workflows that drive revenue, manage risk, and maintain compliance. Built on YOLO for real-time detection, Detectron2 for complex instance segmentation, SAM for zero-shot segmentation, and OpenCV for deterministic preprocessing pipelines.
Combining OCR for layout-aware character recognition, LayoutLM for spatial document understanding, Azure Document Intelligence for enterprise deployments, and LLM-based extraction for documents whose structure requires reasoning, not pattern-matching.
Combining OCR for layout-aware character recognition, LayoutLM for spatial document understanding, Azure Document Intelligence for enterprise deployments, and LLM-based extraction for documents whose structure requires reasoning, not pattern-matching.
Built on Diffusers for controlled synthetic data generation, classical CV techniques for deterministic processing, and segmentation architectures where pixel-level precision is required — covering the full pipeline from acquisition to downstream use.
Centred on Whisper for high-accuracy multi-language transcription, Deepgram for sub-300ms streaming transcription, ElevenLabs for enterprise voice synthesis, and custom diarization pipelines for contact centre and compliance use cases.
Combining multimodal vision-language models for semantic understanding, streaming inference pipelines for low-latency frame-by-frame processing, and specialised architectures for object tracking, action recognition, and event detection across long-duration recordings.
Computer vision at enterprise scale means integrating visual inference into the workflows that drive revenue, manage risk, and maintain compliance. Built on YOLO for real-time detection, Detectron2 for complex instance segmentation, SAM for zero-shot segmentation, and OpenCV for deterministic preprocessing pipelines.
Combining OCR for layout-aware character recognition, LayoutLM for spatial document understanding, Azure Document Intelligence for enterprise deployments, and LLM-based extraction for documents whose structure requires reasoning, not pattern-matching.
Combining OCR for layout-aware character recognition, LayoutLM for spatial document understanding, Azure Document Intelligence for enterprise deployments, and LLM-based extraction for documents whose structure requires reasoning, not pattern-matching.
Built on Diffusers for controlled synthetic data generation, classical CV techniques for deterministic processing, and segmentation architectures where pixel-level precision is required — covering the full pipeline from acquisition to downstream use.
Centred on Whisper for high-accuracy multi-language transcription, Deepgram for sub-300ms streaming transcription, ElevenLabs for enterprise voice synthesis, and custom diarization pipelines for contact centre and compliance use cases.
Combining multimodal vision-language models for semantic understanding, streaming inference pipelines for low-latency frame-by-frame processing, and specialised architectures for object tracking, action recognition, and event detection across long-duration recordings.
YOLO, Detectron2, Whisper, LayoutLM — we build on frameworks with proven track records at enterprise throughput, not experimental models that break in production.
Deep expertise across computer vision, IDP, speech, and video modalities — with a unified architecture that connects them rather than treating each as a siloed point solution.
Azure OpenAI, Amazon Bedrock, Vertex AI — deployed within your own subscription. No shared infrastructure. Full data residency compliance from day one.
A named technical contact — not a ticket queue. Drift monitoring, scheduled performance reviews, and retraining on new data as part of every ongoing engagement.
Working software at the end of every two-week sprint. Signed evaluation reports before production. A deployment runbook and 30 days of post-launch hypercare support.
ISO 27001, SOC 2 Type II, GDPR, HIPAA, and AMLD compliance built into the architecture — not bolted on after deployment. Guardrails, audit logging, and PII redaction included.
YOLO v8/v9/v10
Detectron2
SAM
OpenCV
Diffusers (HF)
Tesseract OCR
PaddleOCR
LayoutLM v3
Azure Doc Intelligence
GPT-4o / Claude
OpenAI Whisper
Deepgram
ElevenLabs
pyannote.audio
GPT-4o Vision
Gemini 1.5 Pro
FFmpeg Pipeline
ByteTrack
Azure OpenAI
Amazon Bedrock
Vertex AI
MLflow / DVC
Triton Inference Server
Pinecone / Weaviate
Best-in-class inference speed for production-line and live-feed scenarios. Sub-50ms detection on high-resolution imagery.
Facebook AI Research architecture for complex instance segmentation. Strong ecosystem and enterprise track record.
Zero-shot segmentation reduces labelling overhead on novel object classes with strong generalisation across domains.
Token + layout joint modelling — critical for extracting structured data from variable-format invoices and contracts.
High accuracy across 90+ languages. On-premises deployable for strict data residency requirements in healthcare and finance.
Sub-300ms latency streaming transcription. Purpose-built for production audio pipelines and real-time IVR applications.
Long-context video reasoning and summarisation. Semantic Q&A over footage for automated content review workflows.
State-of-the-art diffusion model access with fine-tuning support on domain imagery for synthetic training data generation.
Spaculus Software is known to get you more than what you think from any Artificial Intelligence development company. Below we have listed a few other AI services you can glance at besides hiring data engineers. Contact us now for the best deals.
An expert contacts you after having analyzed your requirements;
If needed, we sign an NDA to ensure the highest privacy level;
We submit a comprehensive project proposal with estimates, timelines, CVs, etc.
Point solutions process one data type in isolation. The business value of multimodal AI is in the connections between modalities an invoice image that needs OCR, a vendor contract that needs clause extraction, and an approval call recording that needs transcription are three separate inputs to the same accounts payable decision. A coordinated multimodal pipeline handles all three in a single workflow, with a single integration to your ERP and a single audit trail. You get a complete decision, not three separate data extracts that a human must manually reconcile
It depends on the task and the modality. For computer vision on well-defined object classes using fine-tuned YOLO, a few hundred labelled examples per class is often sufficient. For IDP on variable-format documents, the answer depends on format diversity we assess your document sample during the POC data readiness stage. Where labelled data is genuinely scarce, we recommend synthetic data augmentation or a transfer-learning approach from a
pre-trained foundation model. We will not commit to a deployment timeline before we have seen your data.
Yes. Whisper, YOLO, Detectron2, LayoutLM, and Diffusers all run on-premises or in a private cloud environment. For LLM-based extraction and multimodal understanding, Azure OpenAI deployed in your own Azure subscription, or Amazon Bedrock with private endpoints, provides a managed model API that does not route data through shared infrastructure. Healthcare and financial services deployments routinely require this
architecture, and it is our default recommendation for any use case involving biometric, clinical, or financial personal data.
Every production deployment is configured with accuracy monitoring against a held-out
evaluation dataset that is periodically refreshed with production examples. When drift metrics
exceed defined threshold typically a drop of more than 2–3% on key accuracy measures an
alert triggers a review cycle. Depending on the root cause, the response is either a prompt or
threshold adjustment, fine-tuning on new production data, or a more substantive retraining
engagement. Retraining cadence is defined in the MLOps handover documentation based
on the expected rate of distribution shift in your environment.
Every production pipeline includes a confidence threshold below which the system escalates to a human reviewer rather than making an autonomous decision. The threshold is calibrated during evaluation to balance throughput and risk tolerance a higher threshold means more human review but fewer automated errors; a lower threshold increases automation but requires acceptance of a defined error rate. In regulated contexts, all edge cases are routed to a human-in-the-loop queue with the full input, the model’s output, and its confidence score so the reviewer makes an informed decision, not a blind one.
Both structures are available and the right choice depends on the use case. Greenfield
builds typically begin as a fixed-scope project Discovery through Production Deployment
with a defined milestone-based payment schedule. Ongoing model maintenance, retraining,
and roadmap support are structured as a monthly retainer with defined SLA response times
and a named technical contact. We scope both the project and the retainer in the initial
assessment so there are no surprises at the handover stage.
A well-scoped, single-modality POC with defined success criteria typically completes in four to six weeks from data access. Full production build and integration takes eight to sixteen weeks depending on integration complexity, infrastructure constraints, and the volume of human-in-the-loop workflow design required. Multi-modality systems with several integrated pipelines sit at the higher end of that range. We give a tighter estimate after the data
readiness assessment, which is part of the Discovery engagement.
Your organisation generates image, document, audio, and video data every day. The question is whether that data drives decisions or disappears into storage. We scope each engagement in a focused discovery session — no commitment required, and no architecture deck without first understanding your data and your workflow
Yes, AI can be seamlessly integrated into your existing systems, such as CRMs, ERPs, and marketing tools. This ensures enhanced functionality and better performance without disrupting your workflows.
We measure success based on predefined KPIs, such as accuracy, efficiency improvements, cost savings, and ROI. Our team ensures that every AI solution delivers measurable business value.
We adopt a flexible and adaptive approach to address changing business needs. Our team continuously monitors performance, gathers feedback, and makes adjustments to ensure the AI solution remains effective over time.
