Agentic AI

Modernization of legacy systems with engineered intelligence

AI only delivers value when it's engineered into real systems. Atyeti helps you apply AI/ML precisely where it works best—while keeping deterministic guardrails, auditability, and human oversight built in.

The Problem

...89 systems in the US alone, 160 globally. None communicating.- Wallstreet CTO

AI initiatives fail when complexity, governance, and engineering reality are ignored.

Legacy complexity blocks AI adoption

Layered systems and brittle integrations make it hard to deploy new capabilities without breaking what must remain online.

AI proofs don’t reach production

Many initiatives stall at demos because architecture, data readiness, and governance weren’t engineered from day one.

Auditability and risk requirements

Regulated environments need traceable decisions, transparent data pipelines, and defensible outcomes—not black boxes.

Why AI Initiatives Fail

Failure Mode	What It Looks Like	Why It Fails
AI-Native	AI as first solution—where deterministic solutions exist	Complexity of deploying into a very large legacy ecosystem
Framework Lock-in	Branded proprietary frameworks and generic tools that lock you to a vendor	"5% of enterprise vendor tools reach production" (MIT, GenAI Divide 2025)
No architecture, just ambition	AI adopted without decomposed solution architecture or governance pipelines—"AI will figure it out"	"95% of organizations are getting zero return. This divide seems to be determined by approach" (MIT, 2025)

Atyeti's Agentic AI Solution

Engineered intelligence: deterministic guardrails where certainty exists, AI/ML where ambiguity demands it.

Deterministic pushdown + targeted AI/ML

Use deterministic systems for rules, validation, orchestration, and audit trails—apply AI for semantic judgment, pattern recognition, and fuzzy matching.

Forward-deployed engineering delivery

Senior builders embedded through discovery and production delivery. Success is measured in production metrics, not slide decks.

Responsible, explainable, governed AI

Human oversight workflows, monitoring, and explainability designed in—so outcomes are reliable, defensible, and maintainable.

Responsible, Auditable AI Design Philosophy

Use Deterministic Systems For	Use AI/ML For
Business rules, validation logic	Semantic judgment on unstructured data
SQL queries, config-driven pipelines	Pattern recognition humans can't codify
Orchestration, workflow execution	Fuzzy matching, entity resolution
Audit trails, compliance checks	Natural language understanding

High-Velocity AI Requires a Modern Data Supply Chain

The Problem: The "Data Bottleneck"

Most organizations have plenty of data, but it's trapped in rigid, legacy systems. You cannot fuel Generative AI or Predictive Analytics with a "batch-processed" supply chain. If your data is siloed, messy, or slow, your AI initiatives will be inaccurate, expensive, and ultimately fail to scale.

The Atyeti Solution: Engineering the AI-Ready Enterprise

Databricks

Architecting the Lakehouse

Unify data engineering and data science with governed, real-time pipelines.

Unity Catalog
Robust governance — a single, secure source of truth for AI models.
Delta Live Tables
Always-on pipelines transforming raw data into ML-ready features in real time.

Snowflake

Scaling Global Analytics

Massive scale and zero-management overhead at the consumption layer.

Snowpark
Move AI/ML processing to the data — eliminate cost and security risks of data movement.
Data Sharing
Seamless, secure collaboration across global business units without complex ETL.

BigQuery

The Analytics Powerhouse

The central nervous system for your enterprise data estate.

BigQuery ML
Build and deploy ML models using standard SQL — shorten the path from data to prediction.
BigLake
Query data in Databricks or Snowflake without moving it — a unified multi-cloud view.

Gemini Enterprise Agent Platform

The AI Integration Layer

Orchestrate the entire ML lifecycle so data becomes truly model-ready.

Feature Store
Centralized AI feature repositories ensuring training/production consistency.
AutoML
Google's world-class algorithms accelerate custom model creation.

The Atyeti Value Proposition

We Modernize

→

So You Can

Legacy Silos

Create a unified Data Fabric accessible to every AI agent.

Manual Data Pipelines

Achieve Continuous Data Delivery with 99.9% reliability.

Stale Datasets

Power Real-Time AI that responds to market shifts in seconds.

Fragmented Security

Ensure Enterprise-Grade Governance across all AI training data.

The "Agile Engineering" Edge

AI is only as good as the data feeding it. Atyeti provides the Software, Data, and Platform Engineering expertise to turn your data complexity into a competitive AI advantageLearn more

From Legacy to Production: How We Work

Senior domain practitioners, embedded from discovery through delivery. No handoffs.

Discover & Assess

Map your systems landscape, identify high-impact AI opportunities, and evaluate data readiness. Senior domain practitioners embedded from discovery.

Architect & Design

Engineer a production-ready architecture with deterministic guardrails, governance frameworks, and clear AI/rule boundaries. No handoffs, no context loss.

Build & Deploy

Forward-deployed engineers deliver production-grade ML pipelines, model serving infrastructure, and monitoring. Success measured in production metrics, not slide decks.

Measure & Optimize

Deployment velocity, system uptime, processing time reduction, and cost-per-transaction impact. Models monitored for drift and retrained automatically.

Solving Problems Frameworks Can't Touch

These aren't "AI implementations." They're novel architectures where AI is one precisely-scoped component.

Transaction Reconciliation

From weeks-long processes to automated, near real-time execution. >99% precision, 8hrs→1.5min inference, 4 weeks→2 hours onboarding.

>99% precision8hrs → 1.5min2hr onboarding

Fixed-Length Schema Discovery

Semantic segmentation on character streams—row classification, character mask, automatic schema detection. 2+ weeks manual mapping eliminated.

2 weeks → minutesAuto schemaZero manual

Enterprise Agentic Data Federation

Opt-in agentic architecture with no central authority or schema mapping required. Anti-fragile queries across massively disparate systems.

O(n²) → O(1)Anti-fragileSelf-documenting

AI Break Matching

Transformer embeddings → FCNN → per-account vector model with neighbor-aware density clustering. Models monitored for drift and retrain automatically.

Continuous learningO(n) runtimeAuto retrain

Regulatory Reporting Platform

Modern data pipelines, human oversight, and AI/ML-assisted anomaly detection for regulatory reporting. 15+ global regulatory bodies supported.

15+ regulatorsEvent-drivenImmutable arch

Reconciliation Auto-Matching

Reduce manual effort, optimize rule accuracy, and automate exceptions. ML-driven root-cause analysis with recommended rules for resolution.

55% MTP reductionAuto rulesRoot-cause ML

Outcomes

Move from AI experimentation to production impact with an engineering-first approach
Reduce operational friction and manual effort with reliable, auditable automation
Modernize without “rip-and-replace”—deploy AI on top of systems that must remain online

Ready to operationalize AI?

We'll help you identify the right use cases, engineer a defensible architecture, and ship to production.

Talk to an expert