Talentica > Enterprise > Enterprise AI Services
Enterprise AI Services
Design, deploy, and govern AI products, copilots, and agents across business-critical systems.
The Problem
A model isn't a product
Enterprises may be building new AI products, adding agentic capabilities, or moving initiatives forward when internal teams have limited bandwidth. Getting AI to work in a demo is only one part of the effort. The rest covers use-case definition, system engineering, integration, testing, and governance. Each adds different technical and operational demands, and any one of them can affect how quickly an AI initiative moves into production.
What we do
Enterprise AI,
end-to-end
Enterprise AI rarely comes down to one project. It spans six areas, from strategy to the governance around it.
Identify where AI can create value and define the technical and business path to get there.
- AI opportunity & use case discovery
- AI readiness assessment
- Business case & ROI analysis
- AI architecture strategy
- Build vs. buy evaluation
- AI roadmap & prioritization
Design and build AI-powered products and features for real users, workflows, and operating conditions.
- AI product design & prototyping
- GenAI application development
- Model development & fine-tuning
- RAG & knowledge systems
- AI feature development
- AI product modernization
Build copilots that bring enterprise knowledge, tools, and workflows into the flow of work.
- Knowledge & search copilots
- Developer copilots
- Operations copilots
- Customer support copilots
- Workflow & productivity copilots
- Custom enterprise copilots
Design and engineer agents that can reason, act, and work across enterprise systems and processes.
- Agent discovery & design
- Single & multi-agent systems
- Agentic workflow automation
- Tool & system integration
- Agent evaluation & testing
- AgentOps & observability
Build the data, infrastructure, operations, and observability needed to run AI reliably at scale.
- AI-ready data pipelines
- Model serving & inference
- MLOps & LLMOps
- AI infrastructure & scaling
- Model & prompt management
- AI observability & performance
Put the controls, oversight, security, and auditability around AI needed for enterprise use.
- AI governance frameworks
- AI security & privacy
- Model risk assessment
- Guardrails & safety controls
- Human-in-the-loop controls
- Compliance & auditability
How we work
We build to run in production
We move from use case to running system, engineering for real data, real scale, and real users along the way. We leave nothing as a prototype waiting to be rebuilt.
Start with the use case
We pick problems where AI delivers value, then build for them.
Build the system around the model
This includes data, pipelines, integration, and ops.
Test AI the way AI needs testing
Evaluations, guardrails, and real-world data before it ships.
Small teams that own the outcome
We work with focused pods with regular engineers, not contractors.
Governed from the start
Safety, oversight, and compliance built in.
Case Study
AI systems in the real world
Platform
A control plane for AI agents
As the company scaled AI adoption, agents proliferated without governance. No central registry, no standard invocation layer, and no shared security model. Every new agent meant its own deployment artifact, credentials, and runbook, so the tenth agent cost nearly as much as the first.
- A control plane (FastAPI + Cloud SQL) that separates agent registration and configuration from execution on Vertex AI Agent Engine or external A2A infrastructure
- A single slug-routed invocation gateway for every agent type, with routing driven entirely from the database
- A canvas-based agent editor with local testing via an ADK InMemoryRunner before any GCP deploy
- Centralized security—RS256 JWT, Fernet-encrypted credentials, with per-tenant visibility governance
- Versioned deploys with config snapshots and single-click rollback
Three previously independent AI capabilities, Makegood Generation, Document Extraction, and the Global AI Plan Builder, were consolidated onto one governed platform. New agents go live in minutes with zero code changes, credential rotation needs no redeploy, and business users can tune agent behavior without an engineering request.
Platform
Agentifying a monolithic platform
A 10-year-old legacy monolith spanning .NET, Node.js, SQL Server and PostgreSQL needed to move from having no AI to being fully agentic in time for a marquee customer launch within six months.
- Discovery covering agentic workflows, experience design, APIs, and data sources
- AI-ready foundations: services with APIs, a data lake, an agent-orchestration framework, and test coverage
- A pilot content-creation task agent with human-in-the-loop review
- Testing across dataset preparation, LLM evaluation, latency, and accuracy
- Production rollout of the content-curation workflow with feedback loops and evals
The platform went from no AI to a fully agentic product in 100 days, shipping 15 agents and 60 features and securing a marquee customer signup within the 6-month window.
Our partners
Tools & technologies
LLM & Generative AI
Core AI/ML & Data Engineering
DevOps & Cloud
Observability & Monitoring
Security & Governance
Turn AI into a working capability
Build internal AI systems, enterprise agents, or AI-powered products designed for production.