AI/ML Development
Production AI, not proof-of-concept
We build AI systems that ship, scale, and stay reliable in production. From RAG applications and agentic workflows to MLOps pipelines and AI infrastructure — engineered for enterprise, not just demos.
What We Deliver
Core Capabilities
- RAG Applications & Enterprise Knowledge Bases
- Agentic Workflows & AI Orchestration
- MLOps — Model Training, Deployment & Monitoring
- AI Infrastructure — Vector DBs, GPU Compute & Serving Pipelines
- LLM Integration & Fine-Tuning
- Generative AI Products — Copilots, Chatbots & Automation
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By the Numbers
What clients achieve with GYSP
the baseline we target before any AI application goes live — not demo accuracy
on target processes after agentic workflow deployment in production
demo-to-production gaps closed by design, not discovered by users
Proven Results
AI/ML Case Studies
FinTechGlobal Financial Services Group
A global financial services group was spending significant analyst bandwidth on manual P&L reconciliation across 4 disparate data systems, with no anomaly detection, no forward-looking forecast, and regulatory reports still produced from static spreadsheets. GYSP unified the data layers on ODI 12c, reduced reconciliation work by 60%, deployed OAC ML anomaly detection on live P&L pipelines, and built rolling 3-month commercial forecasting — all within a single integrated analytics architecture.
Logistics & Supply ChainNational Rail & Logistics Authority
A national rail and logistics authority was running operational and financial analytics on an aging OBIA and OBIEE stack approaching end of supportable version — with Informatica ETL pipelines extending overnight batch windows and no forward-looking ML capability for planners. GYSP executed the full lifecycle upgrade to OBIEE 12c with zero downtime, migrated ETL to ODI 12c, and embedded predictive demand and asset maintenance models into the analytics core.
EdTechGen-AI Education Platform
Users were spending hours manually searching across PDFs, videos, and web pages. The question: could a single AI interface replace all of it — accurately, at enterprise scale?
Client Voices
What our clients say
“Team GYSP helped us take an idea and turn it into a trading tool traders actually love. Their forecasting engine, risk-reward dashboards, and clean UX made strategy testing faster and decision-making easier. We've seen higher engagement and trust from our user base thanks to their precise execution.”
“We were drowning in unstructured freight documentation — PDFs, emails, contracts in three languages. GYSP built a RAG pipeline that extracts, classifies, and routes everything automatically. What two full-time staff handled daily now runs in 35 minutes with 93% accuracy. The ROI case closed itself in the first week of production.”
“We needed to replace a 15-year-old rules engine with a production-grade ML risk model. GYSP rebuilt the entire MLOps pipeline — feature engineering, training, deployment, and automated retraining — and gave us explainability tooling our actuaries could use in regulatory submissions. Underwriting speed improved 3x in the first quarter.”
FAQs
Common questions
Everything buyers typically ask before starting a ai/ml engagement.
Ask us anythingHow do you ensure AI systems work in production, not just demos?
We build for production from day one — rigorous evaluation frameworks, load testing, fallback handling, monitoring pipelines, and a defined accuracy threshold (85%+ on production corpora) before any system goes live. Demo performance and production performance are measured separately.
What's the typical timeline for building a RAG application?
A well-scoped RAG application — ingestion pipeline, retrieval layer, evaluation framework, and a chat interface — typically takes 8–12 weeks from kick-off to production-ready. Complex enterprise knowledge bases with multiple data sources take 12–20 weeks.
Do you fine-tune foundation models or use them out of the box?
Both, depending on the use case. Most enterprise applications achieve strong results with prompt engineering and RAG before fine-tuning is needed. We recommend fine-tuning only when the base model consistently fails on domain-specific tasks where retrieval alone isn't sufficient.
How do you handle data privacy when building AI systems that process sensitive data?
We design for data minimisation from the start — on-premise or VPC-deployed models where required, strict PII handling in ingestion pipelines, role-based access to vector stores, and audit logging throughout. Compliance requirements (HIPAA, GDPR, financial data) are scoped before architecture is finalised.
What does an agentic workflow actually look like in practice?
An agent is a loop: perceive input → reason → select tool → execute → observe result → repeat. We build these with defined tool sets, guardrails, memory management, and human-in-the-loop escalation for edge cases. In practice, a customer query arrives, the agent classifies it, retrieves context, drafts a response, checks it against policy, and either sends or escalates.
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