We build production-ready AI/ML systems — from custom model development to fully orchestrated agentic AI workflows. Proven outcomes: 85% time savings, 90% detection accuracy.
ToDo IT Services designs and engineers AI/ML systems that go beyond prototypes and deliver measurable business impact. Our engineers work across the full stack of machine learning — from raw data pipeline design to deploying models in production environments at scale.
We select tools based on your specific requirements — not our preferences. Our engineering team is proficient across the full AI/ML ecosystem:
We start by understanding your business problem, not the technology. What decisions do you need AI to make? What data do you have? What does success look like in measurable terms? This scoping phase prevents the most common AI project failure: building the wrong thing precisely.
We audit your existing data for completeness, quality, and relevance. We design ingestion pipelines, define preprocessing steps, and establish ground-truth labeling requirements if applicable. This is where most projects either succeed or fail — we invest heavily here.
Rapid prototyping with experiment tracking. We test multiple model architectures, evaluate against baseline metrics, and iterate. All experiments are logged in MLflow for full reproducibility. You review results at every checkpoint — no black boxes.
Wrapping the model in a reliable API, setting up monitoring for model drift and inference latency, and integrating into your existing systems. We provide full documentation and knowledge transfer.
Reduction in manual processing time — Transaction Reader project. Our ML-based transaction classification system eliminated manual data entry for 19,000+ monthly transactions.
OCR accuracy on handwritten documents — Notiva project. Our computer vision pipeline converts handwritten text to digital format with near-human accuracy.
Fraud detection accuracy — Transaction monitoring system built for financial sector client. Reduced false positives by 60% compared to rule-based predecessor.
Reduction in manual effort — Order form automation project processing 19,000+ orders monthly with AI-assisted classification and routing.
Scope determines timeline. A focused proof-of-concept takes 4-6 weeks. A full production system with data pipelines, model training, API layer, and monitoring typically takes 3-4 months. Agentic AI workflows with multiple integrated agents average 6-10 weeks from scoping to deployment.
Not always. For many use cases, we can work with 500-2,000 labeled examples using transfer learning and data augmentation. We'll assess your data during the discovery phase and tell you honestly whether you have enough to train a reliable model or whether synthetic data generation is needed.
An agentic AI system uses multiple AI agents — each with a specific responsibility — that work together to complete complex multi-step tasks autonomously. Examples include our LinkedIn case study automation (research → drafting → review → publish) and our ETC monitoring bot. You need one when a single AI call isn't enough to complete a task reliably.
All client data is processed under strict confidentiality agreements. We can work with on-premise deployments where data never leaves your infrastructure, private model hosting on your cloud account, and data anonymization pipelines before any model training. We never use client data to train models for other clients.
Yes — and this is often the most practical approach. We build AI as an API layer that plugs into your existing systems via REST or GraphQL endpoints. Your current application keeps working; AI capabilities are added without rebuilding your stack. We've done this for web platforms, mobile apps, ERP systems, and Slack integrations.
Explore related case studies: Transaction Reader (85% time saved) | OCR Pipeline (92% accuracy) | Order Automation (60% effort reduction)
Tell us your problem. We'll tell you honestly if AI is the right tool — and if it is, we'll build it to measurable outcomes.
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