/ Applied AI & ML
Applied AI & ML: From GenAI Pilots to Production
ACI Infotech takes AI from pilot to production: GenAI assistants, forecasting engines, and recommendation systems that run around the clock with SLAs. Every model ships with MLOps pipelines, monitoring, and ArqAI governance, so it keeps working after launch and holds up when an auditor asks how it decides.
- 50+ AI models in production
- ArqAI governance platform
- MLOps on every model
- ISO 27001
Frontier models, governed
Delivered with our strategic partner ArqAI.
From pilot to production
Production AI starts with
the unglamorous part.
Evaluation gates, guardrails, drift monitoring, and an answer ready for the auditor who asks why the model said what it said. Build that layer first and the pilot graduates: the model keeps working, the business keeps trusting it, and the system is still earning its keep a year later.
30%Improvement in forecast accuracy
Forecasting engines retrained daily at a Fortune 500 bank
Financial Services · A Fortune 500 retail and commercial bank
Read the case study/ What we build
Production AI. Six ways in.
01
GenAI and LLM solutions
Enterprise assistants, document processing, and code generation on Azure OpenAI, AWS Bedrock, Claude, or private LLMs in your own tenancy. Retrieval, guardrails, and evaluation gates come standard, because a chatbot without them is a liability with a chat window. Support assistants built this way have cut ticket volume by 20%.
Azure OpenAIAWS BedrockClaudeLangChain
Azure OpenAI
AWS Bedrock
Claude
LangChain02
Predictive analytics
Forecasting engines for demand, churn, and risk that retrain themselves as the data moves. Predictions get served in production, in real time, not from a notebook someone remembers to run on Fridays.
Databricks MLPythonTensorFlowscikit-learn
Databricks ML
Python
TensorFlow
scikit-learn03
Recommendation systems
Personalization engines for retail, media, and financial services, serving recommendations in real time with A/B testing built in. Clients have measured conversion lift of 15% once the engine went live.
Spark MLlibTensorFlow RecommendersFeature stores
Spark MLlib04
MLOps and model management
CI/CD for models: automated testing, deployment, monitoring, and retraining on MLflow, Kubeflow, or SageMaker. Teams deploy model updates 2 to 3x faster, and drift gets caught by a pipeline instead of by an executive.
MLflowKubeflowDatabricks Mosaic AISageMaker
MLflow05
AI governance with ArqAI
ArqAI is our own governance platform: policy as code, bias monitoring, drift detection, and audit trails on every model. Systems ship aligned to the EU AI Act and GDPR from day one, so the compliance review is a formality instead of a rebuild.
ArqAIMLflow GovernanceGreat Expectations
ArqAI06
Intelligent process automation
Document AI, intelligent OCR, and workflows that keep a human in the loop where judgment matters. Clients have cut manual processing work by 35% while keeping people on the decisions that need them.
Document AIUiPath AIPower Automate AI
UiPath AI
Power Automate AIGenAI or traditional ML?
Usually the wrong question. GenAI earns its keep on language: documents, copilots, agents that read and write. Classic ML still wins on numbers: forecasting, scoring, optimization, anywhere you need a measurable error rate. Most estates need both, running on one MLOps spine so governance is built once instead of twice. We build either, and we will tell you which fits before writing a line of code.
GenAI
Documents, copilots, and conversational agents
Search and summarization over your own content
Most enterprise estates, honestly
Both platforms
Classic ML
Forecasting, scoring, and optimization
Fraud, churn, and risk models with hard accuracy targets
Most enterprise estates, honestly
Both platforms
A card under both columns means both, under one MLOps spine and one governance layer.
/ Results
Shipped, governed, still running.
Fortune 500 Retail Client
87%Reduction in Data Processing Time
Databricks Modernization & AI Enablement for a Leading Convenience Retail Chain
Read the case study
Fortune 500 Financial Services Client
83%Reduction in Model Deployment Time
Driving Enterprise Data Transformation with ACI’s Azure Lakehouse
Read the case study
Fortune 500 Transportation & Logistics Client
$32MAnnual Revenue Impact
ACI Infotech Drives Refinery Excellence with KPI-Led Digital Transformation
Read the case study/ How an engagement runs
Five phases. No mystery.
01
Scope
Weeks 1 to 2
One use case, chosen for value and data availability, not for the demo it would make at a board meeting.
02
Ground
Weeks 2 to 5
Data readiness, retrieval design, and the governance model. If the data is not ready, we say so now, not in week ten.
03
Build
Weeks 4 to 10
Model, pipeline, and guardrails shipped in increments, with evaluation gates before anything faces a user.
04
Prove
Weeks 8 to 14
Evals against agreed thresholds, bias checks, and human sign-off. Production access is earned, not assumed.
05
Run
Monitoring, drift detection, and retraining under SLA with our managed team, or handed to yours with runbooks.
Launch is when the model starts aging.
Data shifts, prompts rot, and last quarter's accurate model quietly becomes this quarter's wrong one. Our managed operations team watches production models around the clock: drift detection, retraining pipelines, and P1 response in fifteen minutes when something misbehaves at 2am.
Managed Operations/ Why ACI
Why enterprises pick us for AI
/ Delivery
The architects who scope your model are the ones who ship it.
Nobody hands your project to a bench.
/ Governance
ArqAI, our own governance platform: policy as code, bias monitoring, and audit trails aligned to the EU AI Act and GDPR.
/ Scale
Founded 2006.
1,200+ engineers across 11 global delivery hubs. 50+ AI models in production.
/ Operations
ISO 27001 certified, with production models monitored 24/7 under published SLAs.
/ Questions
AI questions,
answered straight.
The questions we hear most before an AI engagement. Anything else belongs in a conversation.
What does a production GenAI implementation require?
More than a prompt and an API key. You need governed data for retrieval, a RAG or agent design that fits the job, evaluation before launch, and guardrails and monitoring after. The model is the easy part.
How long does an AI project take?
A scoped use case with data in reasonable shape reaches production in 8 to 14 weeks. Complex ML systems with custom models run 6 to 12 months. If the data is not ready, that work comes first, and we will say so plainly rather than paper over it.
How do you handle AI governance and compliance?
Through ArqAI, our purpose-built governance platform: policy as code, automated bias monitoring, drift detection, and audit trails. Systems ship aligned to the EU AI Act, GDPR, and your industry rules from day one, not retrofitted before an audit.
Can we use private LLMs for sensitive data?
Yes. We deploy private LLMs in your own environment, on Azure OpenAI, AWS Bedrock, or on-prem, with full data residency controls. Your data never leaves your tenancy.
What about model drift and retraining?
Every model ships with drift detection and a retraining pipeline. Most refresh daily or weekly depending on data velocity, and you watch performance on live dashboards instead of taking our word for it.
How do you pick the first AI use case?
We look for a job with clear value, available data, and room to be wrong sometimes. Chasing the flashiest use case first is how pilots die. Better to ship one that pays for itself and earns the next.
Who owns model risk and governance?
We build the controls in: model registry, versioning, audit trails, and policy checks, with clear ownership on your side. Regulated buyers have to show their work, and bolting that on afterward never goes well.
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