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AI & Analytics · ML Engineering · LLM · MLOps · Automation
AI That Actually Runs in Production.
Not Just in a Notebook. The gap between an AI proof-of-concept and a production AI system is wider than most organisations expect. Symhas bridges that gap — building, deploying, and operationalising machine learning models inside your Oracle and cloud environment, with monitoring, retraining, and integration into your live business processes. Custom ML model development. Large language model (LLM) integration. MLOps infrastructure. Intelligent process automation. All production-ready in 12 weeks, fixed price.
↓40% Manufacturing
Reduction in manual processing time from AI-automated document and data workflows
94% Cross-sector
Average model accuracy on business classification and prediction tasks at production handover
3× Financial Services
Faster credit underwriting using ML-assisted decisioning on Oracle Banking data
12wk All Sectors
Proof-of-concept to production — fixed price, fixed timeline, your environment
Part of the Symhas AI & Analytics practice
94% Average model accuracy on classification and prediction tasks at production handover
↓40% Average reduction in manual processing time from intelligent automation deployments
12wk From problem definition to production AI system — all environments, all industries
0 AI projects abandoned mid-flight — every Symhas engagement completes to production
What We Deliver
Core Capabilities.
All Production-Ready.
Custom ML Model Development Classification · Regression · NLP · Computer vision
End-to-end ML model development — from problem framing and data preparation through model selection, training, validation, and production deployment — built on your enterprise data.
Classification models — document routing, fraud detection, quality inspection
Regression and time-series models — forecasting, pricing, capacity planning
NLP models — contract extraction, sentiment analysis, document classification
Computer vision — manufacturing defect detection, document digitisation
Ensemble and reinforcement learning for complex optimisation problems
→ Every model includes: explainability tooling, confidence intervals, drift monitoring, and a retraining pipeline
Large Language Model (LLM) Integration GPT-4 · Claude · Gemini · Private deployment
Enterprise-grade LLM integration — connecting foundation models to your Oracle ERP data, internal knowledge bases, and business processes — with data privacy, access control, and hallucination guardrails built in.
Retrieval-Augmented Generation (RAG) on your Oracle and enterprise data
Internal knowledge base Q&A and document intelligence
Contract and document extraction and summarisation
AI-assisted report generation integrated with Oracle Analytics
Private LLM deployment options for regulated data environments
→ LLM outputs connected to Oracle workflows — not standalone chatbot interfaces
MLOps Infrastructure & Platform Engineering CI/CD · Model registry · Monitoring · Retraining
The infrastructure that keeps AI systems reliable, accurate, and maintainable in production — model registries, automated retraining pipelines, drift detection, and CI/CD for ML workflows.
Model registry and versioning on OCI, AWS, or Azure
Automated retraining triggers — schedule-based and drift-triggered
Real-time model performance monitoring and alerting
Feature store design and implementation
ML pipeline CI/CD — testing, validation, and staged rollout
→ Production models that maintain accuracy over time — not systems that degrade silently
Intelligent Process Automation IPA · RPA + AI · Document intelligence · Workflow
Combining AI with process automation to eliminate structured manual work — document processing, data extraction, approval routing, exception handling — integrated directly into Oracle Fusion workflows.
Intelligent document processing — invoices, contracts, purchase orders
AI-assisted Oracle Fusion workflow automation and exception routing
Optical character recognition with ML extraction validation
Automated data reconciliation across Oracle and third-party systems
Human-in-the-loop escalation design for low-confidence cases
→ Typical outcome: 40–70% reduction in manual processing time for targeted document and data workflows
How We Work
From Assessment to
Production in 12 Weeks.

Every engagement follows a structured four-phase delivery model with defined go/no-go gates at each milestone. Fixed price. Fixed timeline.

01
Problem Framing & Data Assessment Weeks 1–2

Define the AI use case precisely. Audit data availability, quality, and labelling. Agree on success metrics, accuracy thresholds, and business integration points before any model work begins.

02
Model Development & Validation Weeks 3–8

Feature engineering, model selection, and training. Iterative validation against held-out data. Explainability tooling configured. Business stakeholder review at Week 6 with accuracy checkpoint.

03
Production Deployment & Integration Weeks 9–10

Model deployed to production infrastructure. API integration into Oracle Fusion or cloud services. Monitoring dashboards live. Retraining pipeline configured and tested.

04
Certification & Handover Weeks 11–12

Data science team trained on model monitoring, retraining, and interpretation. MLOps documentation complete. Symhas moves to quarterly advisory. Production SLAs active.

Financial Services · ML Decisioning $25B AUM Asset Manager.
AI Credit Decisioning. 3× Faster. Same Risk Profile.

A $25B AUM financial services organisation was processing credit and counterparty risk assessments through a manual underwriting process that took an average of 4.5 days per application — creating a capacity bottleneck during high-volume periods and a competitive disadvantage in time-sensitive markets.

Symhas deployed an ML-assisted credit decisioning layer on Oracle Banking and Fusion Finance data — generating risk scores, counterparty summaries, and recommended decisions for underwriter review, with full explainability for regulatory audit trails.

3× Faster underwriting cycle
94% Model decision accuracy
↓81% Manual data preparation time
12wk Concept to production
Discuss Your Programme
What was delivered

Oracle Banking + ML Credit Decisioning — Production System

ML risk scoring model trained on 8 years of Oracle Banking loan performance data
Real-time counterparty data enrichment from internal and external feeds
SHAP explainability output for every decision — regulatory audit trail ready
Underwriter review interface with AI summary and evidence pack
Escalation routing for low-confidence or high-value decisions to senior underwriters
Model performance dashboard — accuracy, drift, and decision distribution monitored daily

"The model gives our underwriters the analysis they used to spend three days assembling — in 90 seconds. They spend their time on judgment, not data preparation."

— CRO, Global Asset Management Firm
Technology Stack
Platform-Agnostic.
Best Tool for the Job.

We are not tied to any vendor. We select and implement the right technology for your environment, your data, and your team.

Oracle Oracle Analytics Cloud Embedded AI, AutoML, model serving
Oracle Oracle Data Science OCI-native model development and MLOps
LLM OpenAI GPT-4 / Claude Foundation model integration and RAG pipelines
ML Platform AWS SageMaker Model training, registry, and deployment
ML Platform Azure ML / Databricks MLOps pipelines and feature engineering
Monitoring Evidently AI / Arize Model drift detection and performance monitoring
Automation UiPath / Power Automate RPA integration for intelligent process automation
Vector DB Pinecone / pgvector RAG knowledge base and semantic search
Why Symhas
AI Expertise Built from
Production Deployments.
From Notebook to Production — Every Time 90% of AI projects fail to reach production. Every Symhas AI engagement ends with a running production system — deployed in your cloud, integrated into your Oracle workflows, with monitoring and retraining active from day one of handover.
Data Privacy and Regulatory Compliance Built In LLM and ML deployments in regulated industries require careful data handling. Symhas configures private LLM deployment options, data masking, access controls, and audit logging — so AI capability does not create compliance exposure.
Oracle Integration Expertise Most ML vendors treat Oracle ERP as a data export. Symhas integrates AI models directly into Oracle Fusion workflows — so model outputs surface in purchase approvals, risk reviews, and financial processes where they drive decisions.
Accuracy Commitment Before We Start We define the minimum acceptable accuracy threshold at the start of the engagement. If the Week 8 checkpoint does not meet the agreed threshold, we continue until it does — within the same fixed price.
MLOps That Keeps Models Accurate AI systems degrade without maintenance. Every model we deploy includes automated drift detection, scheduled retraining, and performance monitoring — so accuracy is maintained without ongoing manual intervention.
No Vendor Lock-In We are model-agnostic and platform-agnostic. We select the right foundation model, ML platform, and infrastructure for your specific use case — not for our partner margins. You own every artefact we produce.
Also in AI & Analytics
Explore the Other
AI & Analytics Capabilities.
Data Platform & Governance

Cloud data platform architecture, data quality engineering, and master data management foundations.
Business Intelligence & Reporting

Self-service BI, automated executive dashboards, and Oracle Analytics Cloud implementations.
Next Step
Tell Us What You Want AI to Do.
We'll Tell You Whether It's Possible With Your Data. A 30-minute AI readiness assessment with a Symhas data scientist and ML engineer. We will review your use case, your data, and your infrastructure — and tell you honestly what is achievable, what the accuracy ceiling looks like, and how long it will take.
Book an AI Readiness Assessment View Client Outcomes
No pitch deck. No sales process. An honest conversation about your data and AI programme.