Sudolabs

Sudolabs works across finance, healthcare, telco, manufacturing, insurance, marketing, entertainment, and customer experience. Its team includes engineers with credentials from Cambridge and CERN, and strategists from BCG and McKinsey.

Contact

Email

hello@sudolabs.io

Offices

• San Francisco: 44 Montgomery St, San Francisco, CA 94104
• Košice: Námestie Osloboditeľov 3/A, Košice Slovakia, 04001

Sudolabs also says clients can meet the team in New York, Prague, and Brussels.

AI Strategy (AI Discovery)

A 4–6 week engagement to validate an AI use-case before building it. It combines technical feasibility and strategic assessment, and produces:

• A prioritised roadmap
• Clickable prototypes
• An ROI model

AI Discovery is intended for new AI initiatives and has mapped more than 500 use-cases.

Custom Agentic AI Platforms

Sudolabs builds and deploys custom AI systems into enterprise environments. These systems are designed around each client's processes, data, and compliance requirements, and commonly include autonomous agents, multi-agent orchestration, and workflow automation.

• Typical duration: 4–12 weeks
• Output: a live system in the client's environment
• Best for: validated use-cases
• More than 100 production AI systems deployed

Engagement options

Clients can:

1. Start with AI Discovery and then move into production.
2. Go directly to production when the use-case is already validated.
3. Engage Sudolabs for ongoing AI development after the first system is live.

Agentic AI

Autonomous agents that act, not just answer, including:

• Multi-agent orchestration
• RAG and memory architecture
• Decision-support agents
• Human-in-the-loop workflows
• Conversational interfaces
• Content-generation pipelines

Multimodal AI

Cross-modal intelligence spanning vision, audio, and documents, including document intelligence, OCR, object classification, cross-modal reasoning, vision and quality control, audio processing, and video processing.

Predictive AI

Forecasting, detection, and optimisation at scale, including demand forecasting, recommendation engines, ensemble machine learning, anomaly and fraud detection, churn and propensity modelling, and revenue uplift modelling.

Sudolabs AI Platform

Production systems use proven modules, integration patterns, agent architectures, and AI-native user-experience components accumulated across more than 100 deployments. The client system is bespoke, while the underlying foundation is battle-tested.

The platform covers four layers:

• Application: Client-facing AI user experiences, dashboards, and scoring interfaces
• Workflow Intelligence: Multi-agent orchestration, persistent memory, and human-in-the-loop verification
• Model and Inference: Open-source model deployment, RAG pipelines, vector databases, and fine-tuning
• AI Infrastructure: On-premises or private-cloud LLM hosting, GPU cluster configuration, and inference optimisation

1. AI Initiative Long-List

Stakeholder belief audits, data and process reviews, and the compilation of 10–20 candidate use-cases.

2. Technical and Business Assessment

Each use-case is scored against more than 25 criteria. A prioritisation matrix identifies the highest-impact and most feasible opportunities.

3. Practical Verification

Sudolabs evaluates data quality and creates a clickable demo of the selected use-case to validate assumptions before committing to the build.

4. Strategic Roadmap

The engagement produces an implementation timeline with owners, budget estimates, dependencies, and an ROI model.

AI Discovery typically takes 4–6 weeks and ends with a prioritised roadmap and working prototypes. Sudolabs reports a 90%+ realisation rate for Discovery use-cases in production.

• iQor: More than 95% reduction in analyst processing time through AI systems for CX operations. InsightsIQ analyses tens of thousands of calls in 20–30 seconds versus hours of manual reading.
• U.S. Steel: Approximately 5% reduction in energy and emissions costs through predictive optimisation of steel annealing operations, with project investment recovered in under 12 months.
• Major European Insurance Group: 35% reduction in underwriting assessor time by using multimodal document AI to parse insurance PDFs, extract data, and flag non-standard clauses.
• Parapetrol: A machine-learning pricing and inventory system identified €290K of dead stock in €6.6M of inventory across more than 10,000 SKUs.
• GForce: Multi-channel sentiment tracking combined with performance data to predict client churn before revenue is lost across more than 3,000 brands managed by over 50 agencies.
• Brynn.ai: An AI knowledge agent achieved 85% search recall, compared with 46% for Google Drive with Gemini, and saved 15% of daily analyst time.

Sudolabs states that it has created more than $3B in business value across more than 100 production deployments.