Build Agentic Solutions Where
AI Agents Are the
Intelligence Layer.

Not chatbots. Not copilots bolted onto existing software. We build production-grade platforms where multi-agent architectures own domain intelligence — making decisions, orchestrating workflows, and improving with every interaction.

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AI-Native
Products designed around
agent intelligence from day one
17+
Specialized agents in our
most complex deployment
Enterprise
RBAC, audit trails,
governance built in — not bolted on
The Challenge

Building Agentic Products
Is Not an AI Problem.

Most enterprises that attempt to build AI-native products hit the same structural walls — and none of them are about model quality.

🏗️
Architecture, Not Prompts, Is the Hard Part
Calling an LLM API is straightforward. Designing a multi-agent system where agents coordinate, maintain context, respect boundaries, and handle failures gracefully — that's where most teams stall.
🧩
Generic AI Produces Generic Outputs
Without a typed domain model that encodes your business vocabulary, relationships, and rules, AI generates plausible answers — not accurate ones. Domain intelligence doesn't come from the model. It comes from the architecture around it.
🔒
Enterprise Requirements Kill Prototypes
The demo works. Then RBAC, audit trails, multi-tenancy, data isolation, and compliance requirements arrive — and the prototype needs to be rebuilt from scratch. Governance can't be an afterthought on AI products.
📊
AI Without Observability Is a Black Box
When agents make decisions that affect business outcomes, you need to know why. Confidence scores, decision rationale, drift detection, and model health monitoring aren't features — they're requirements.
🔄
Single-Model Bets Are Fragile
Locking your product to one LLM provider creates vendor risk and limits capability. Production systems need model-agnostic orchestration — the ability to swap, combine, and fall back across models without rewriting the product.
⏱️
The Gap Between R&D and Production
Data science teams build brilliant models. Engineering teams build scalable systems. But the gap between a working notebook and a production agent platform is where most AI product initiatives die.
What We Build

Six Capabilities.
One Integrated Platform.

Each capability is a building block. Together, they form production-grade agentic products that work at enterprise scale.

🧠
Multi-Agent Orchestration
Hierarchical agent architectures — orchestrators routing to domain specialists, specialists invoking workers. Agents that coordinate, not compete. Designed for complex workflows where a single prompt can't carry the load.
Agent holarchiesIntent routingContext management
📐
Domain Ontology Design
Typed semantic models that encode your domain's vocabulary, relationships, and business rules. This is what makes AI domain-specialized rather than general-purpose — and what prevents plausible-but-wrong outputs.
Object typesLink typesBusiness rules
🔧
Structured Tool Architectures
Registries of domain-specific tools with defined schemas, execution protocols, and audit trails. Agents invoke tools — not free-form code. Every tool call logged, validated, and traceable.
Tool registriesSchema validationAudit trails
📈
ML Model Integration
Bayesian inference, gradient boosting, deep learning, time-series forecasting, and reinforcement learning — integrated into agent decision loops. Models don't sit in notebooks. They power real-time agent decisions in production.
PyMCXGBoostPyTorchProphet
🔍
RAG & Knowledge Systems
Vector search, GraphRAG, and retrieval architectures that ground agent responses in your enterprise data. Semantic similarity, contextual retrieval, and knowledge graphs that make agents accurate — not just articulate.
Vector searchGraphRAGEmbeddings
🔌
MCP & External Integration
Expose your agent intelligence via MCP servers for external AI systems to consume. Integrate with CRMs, ERPs, analytics platforms, and third-party AI tools through standardised protocols — not point-to-point wiring.
MCP protocolSSE streamingBidirectional sync
The Architecture Framework

Four Layers.
Clean Boundaries. Full Stack.

A repeatable architecture pattern for building agentic products — not a science project, but a framework we've proven in production.

L4
Experience
Layer
API Gateway + Frontend + MCP — User-Facing Intelligence
FastAPI or Node.js backends, modern frontend frameworks, MCP servers for external integration. The layer your users see — chat interfaces, dashboards, planning wizards, and AI-powered workflows.
FastAPINuxt / Next.jsMCP ServerSSE StreamingRBAC
L3
Intelligence
Stack
Agent Orchestration + ML Models + RAG — Decision Engine
Multi-agent orchestration via structured tool-use protocol. Domain-specialized tools, Bayesian models, vector search, and forecasting — all coordinated by an agent hierarchy that routes, decides, and learns.
Claude APIGPT-4GeminiPyMCXGBoostVector SearchGraphRAG
L2
Domain
Model
Typed Semantic Ontology — Domain-Specific Intelligence
Object types, link types, taxonomies, and business rules encoded as structured schema. This is the layer that makes your AI domain-specialized — enforcing relationships and preventing plausible-but-wrong outputs.
Object registryDual-layer taxonomyHierarchy engineBusiness rules
L1
Data
Foundation
Enterprise Data Platform — Single Source of Truth
ACID transactions, governance, compute, and model registry. No secondary databases, no cached CSV fallbacks. One authoritative data layer with medallion architecture, change data feed, and time-travel capability.
DatabricksSnowflakeBigQueryPostgreSQLMongoDB
Technology Stack

Model-Agnostic.
Platform-Flexible. Production-Grade.

We choose the right tool for each layer — not the one we're invested in. Every component is selected for your specific use case, team, and infrastructure.

LLM & Agent Orchestration
Claude API (Anthropic) — deep reasoning, tool use
GPT-4 / GPT-4o (OpenAI) — broad capabilities
Gemini (Google) — large context, multimodal
Llama, Mistral — open-source, on-premise options
LangGraph, CrewAI, AutoGen — framework-flexible
ML & Statistical Models
PyMC — Bayesian inference, probabilistic modelling
XGBoost, LightGBM — gradient boosting, tabular data
PyTorch, TensorFlow — deep learning, custom models
Prophet, NeuralProphet — time-series forecasting
scikit-learn — classical ML, feature engineering
Data & Vector Infrastructure
Databricks, Snowflake, BigQuery — enterprise data
Pinecone, Weaviate, Chroma — vector databases
pgvector — PostgreSQL-native embeddings
MLflow, Weights & Biases — experiment tracking
MCP Protocol — agent interoperability standard
How We Build It

From Vision to
Production-Grade Platform.

The same nine-agent delivery model that powers all our engagements — applied to the specific challenge of building agentic products.

1🔍
Domain Discovery
We map your domain — vocabulary, relationships, decision patterns, and data landscape. The ontology is designed before a single agent is built. This is what makes the intelligence accurate, not just articulate.
2🏗️
Architecture Design
Agent hierarchy, tool registries, ML model integration, data layer, and governance controls — designed as a cohesive system. Every layer has a defined boundary, clean API contract, and independent scalability.
3⚡
Agentic Build
Our nine SDLC agents build your agentic product — agents building agents. ClariX structures requirements, StratiX designs architecture, ForgeX and CraftX generate code, VeriX validates, FleetX deploys.
4📡
Deploy & Evolve
Production deployment with full observability — model health monitoring, drift detection, performance dashboards. Your agent platform doesn't just launch. It learns and improves with every interaction.
Enterprise Governance

AI Products That Your
Risk Team Will Recognise.

When agents make decisions that affect business outcomes, governance isn't a feature — it's the foundation. Every control is built in from day one.

🔒
RBAC & Data Isolation
Role-based access controls with scope-limited data access. Persona-level scoping — from system admin to read-only — enforced at every layer. Multi-tenant isolation with defence-in-depth.
Persona scopingMulti-tenantJWT auth
📋
Full Audit Trail
Every agent action logged with inputs, outputs, decision rationale, and confidence scores. SOC2-ready audit trails. Seven-year retention capability. Every decision traceable end-to-end.
SOC2 Type IIDecision logging7-year retention
👤
Human-in-the-Loop
Agents recommend. Humans decide. Confidence thresholds, mandatory review gates, and explicit escalation triggers ensure humans stay in control of consequential decisions.
Confidence scoresReview gatesOverride logging
🛡️
AI Guardrails
Decision boundaries, authority limits, and fallback modes. Budget thresholds, risk tiers, and scope controls — every agent operates within explicit boundaries. Nothing runs unbounded.
Decision limitsFallback modesAuthority levels
📡
Model Health Monitoring
Continuous monitoring of model performance, agent behaviour drift, and data quality. When models degrade or context shifts, alerts fire before silent failures reach users.
Drift detectionR² monitoringPerformance dashboards
✅
Compliance Alignment
Built for regulated industries. ISO 42001, NIST AI RMF, GDPR, HIPAA, PCI-DSS — compliance validated at the architecture level, not retrofitted when regulators ask.
ISO 42001NIST AI RMFGDPR
Where This Applies

Domains Where Agentic
Architecture Changes Everything.

AI-native products aren't a horizontal play. They're most powerful in domains with complex decision spaces, rich data, and high-value outcomes.

Marketing Operations
AI-Native Marketing Intelligence
Multi-agent platforms that unify planning, budgeting, attribution, and optimisation. Agents that allocate budgets across billions of combinatorial dimensions — producing outcomes no spreadsheet can match.
Budget optimisationBayesian MMMReal-time attribution
Financial Services
Intelligent Risk & Compliance Platforms
Agent architectures that ingest regulatory data, assess portfolio risk, and generate compliance documentation — with full audit trails and human-in-the-loop decision gates for consequential actions.
Risk modellingRegulatory RAGAudit-ready
Healthcare & Life Sciences
Clinical Intelligence Systems
Multi-agent platforms for clinical trial management, drug interaction analysis, and patient journey optimisation — with HIPAA-compliant data isolation and explainable AI outputs.
HIPAA compliantExplainable AIMulti-system orchestration
Supply Chain
Predictive Supply Chain Intelligence
Agentic systems that forecast demand, optimise inventory allocation, and manage supplier risk — integrating real-time signals from logistics, weather, and market data into continuous decision loops.
Demand forecastingInventory agentsRisk signals
Enterprise Operations
Autonomous Operations Platforms
Agent platforms that coordinate across ERP, CRM, HRIS, and custom systems — automating complex multi-system workflows with domain intelligence rather than brittle point-to-point integrations.
Cross-system agentsWorkflow orchestrationGoal-oriented
SaaS & Product Companies
AI-Native Product Features
Embed agentic intelligence into your existing SaaS product — AI assistants that understand your domain, planning wizards that generate recommendations, and analytics that surface insights proactively.
Embedded AIDomain-specialisedMCP-ready
🤝 Live Partnership · In Production

We Didn't Just
Design This.
We're Building It.

MySavi.ai is a greenfield AI-native platform — built from a blank page with agentic architecture from day one. Predikly is the development partner, embedded from the first line of code. 17+ specialised agents orchestrated across a 5-layer holarchy, powered by Bayesian ML models and enterprise-grade governance.

"This isn't a case study written after the fact. It's a partnership we're inside — building, learning, and proving the model in production as we go."
Read the Partnership Story
17+
Specialised AI agents working in concert across the platform
5-Layer
Agent holarchy — orchestrated intelligence, not chatbot chaos
0→1
Greenfield build — blank page to production environment
Live
Active partnership — not a historical case study
For Your Leadership Team

What This Means
in the Room That Matters

For the CTO
This isn't a science project. It's a repeatable architecture framework proven in production. Multi-agent orchestration, typed domain models, structured tool-use, and model-agnostic design — built with the engineering rigour your team expects. Your architects will recognise the patterns. Your ops team will trust the governance.
For the CPO
From concept to production in months, not years. When the architecture is right and the delivery model is agentic, you stop building prototypes that need to be rebuilt for production. The first version is the production version — with governance, security, and scalability already in the foundation.
For the CIO
RBAC, audit trails, multi-tenant isolation, compliance alignment, and model health monitoring — built into the architecture from day one. Your risk and compliance teams won't need to retrofit controls. They're already there. ISO 42001, NIST AI RMF, SOC2 — governance is first-class, not an afterthought.
For the CEO
Your competitors are bolting AI onto existing products. The ones who build AI-native products — where intelligence is in the architecture, not the feature list — create structural differentiation that compounds with every interaction. This is a capability gap that widens over time, not one that closes.
How We Engage

Three Ways to Start

Whether you're building from scratch, embedding intelligence into an existing product, or exploring what's possible — we have a structured starting point.

Greenfield
01
Build an AI-Native Product
A product vision that doesn't exist yet. We design the domain ontology, agent architecture, and data layer — then build it with our nine-agent delivery model. The way MySavi.ai is being built right now.
Embedded
02
Add Agentic Intelligence to Your Product
You have an existing SaaS product that needs AI capabilities. We design and build the agent layer that integrates with your platform — domain-specialised, not generic. Your product, genuinely smarter.
Discovery
03
Agentic Architecture Assessment
Not sure where to start? We assess your domain, data landscape, and product vision — then deliver an architecture blueprint, technology recommendations, and a build plan with defensible estimates.
Next Step

Ready to Build an AI Product
That Actually Works?

A direct conversation about your product vision, your domain, and whether agentic architecture is the right path forward — for your users, your team, your market.

Book a CXO Briefing See Engagement Models

Direct conversation. No pitch decks. No intermediaries.