Right offer.
Right place.
Right time.
Real-time AI personalization, deployed inside your existing stack.
Most enterprise personalization still runs on a 24-hour clock. A high-intent visitor lands on your homepage, the event waits in a data lake overnight, the segment re-scores hours later, and the visitor sees the right experience on their next visit. If they come back. We build the layer that closes that gap, inside the platform you already run, with first proof of value in 4 to 6 weeks.
From overnight batch to same-session response.
A high-intent prospect attends your industry summit, downloads a whitepaper, and visits the pricing page three times in one week. On most B2B sites today, that prospect sees the same homepage as anonymous traffic. The signals are captured. They sit in a data lake until the overnight batch cycle runs. The segment re-scores. The visitor gets a tailored response on their next visit, if they come back at all.
Real-time changes the loop. The signal taps before the batch cycle. A graph joins web, CRM, and intent in near real-time. A model decides the next-best action against the live visitor context. The right experience renders in the same session. Same audience, different orchestration, measurable lift.
- Visitor arrivesEvent captured by your stream.
- Event lands in the lakeOvernight batch load.
- Segment updatesBatch model re-scores the visitor.
- Visitor gets a responseBased on data from yesterday, on their next visit.
- Visitor arrivesStream tapped before the batch cycle.
- Customer Intelligence Graph runsJoins web, CRM, and intent in near real-time.
- Next-Best Action decidesInference on live visitor context.
- Visitor gets the right experienceContent, chatbot, and offer tailored to their intent. Same session.
A modern personalization architecture, built on your stack.
Four layers. Two of them are already running inside your business. We build the two in the middle and connect everything end to end. Governance, consent, and observability are wired in from day one.
Signals
What's already live in your stack.
- Event stream (RudderStack, Segment, mParticle)
- Marketing automation signals
- CRM activity and opportunity stage
- Intent data (6sense, Bombora, ZoomInfo)
- Existing lead-scoring models
- First-party site behavior
Intelligence
The real-time layer connecting it all.
- Customer Intelligence Graph
- Near real-time inference
- Streaming pipelines on your data platform
- Mode-agnostic LLM serving (Claude, GPT, Gemini)
- Vector index per tenant
- Decision logs for every call
Agents
Governed, evaluated, deployable.
- Recommendation Agent (in-session content)
- Next-Best Action Agent (orchestration)
- Brief-to-Assets Agent (campaign generation)
- Conversational Agent (front door, grounded in your taxonomy)
- Eval harness on every release
Activation
Your real-world surfaces, fed from the graph.
- Your website
- Marketer UIs (Marketo, HubSpot, Salesforce)
- CDP destinations (Hightouch, Census)
- Email and chat
- Sales surfaces (Outreach, Salesloft)
Four tensions slowing enterprise AI personalization.
Gartner has flagged each of these. We've seen all four kill production attempts that had real budget and real engineering behind them. Each gets a productized answer in our delivery model.
Data quality is build-your-own.
Lakehouse quality frameworks exist. Every team still assembles their own monitors, drift detection, and signal hygiene. Agents need clean signal as a shipped product, not as a downstream cleanup.
We productize signal hygiene as a versioned layer in your Customer Intelligence Graph. DQ monitors and weekly drift reports ship with every deployment.
The data platform depth is steep.
The platform rewards depth. Business personas don't have time to earn it, and GTM teams cannot wait for them to. Marketing buyers stall in week three because the tooling rewards engineers.
Role-shaped workspaces and pre-built saved queries in front of the buyer. Platform depth stays behind the pod, where it belongs.
Consumption pricing is unpredictable.
FinOps guardrails are not the default. CFOs need a ceiling before a VP of Marketing can sign. A pilot that bills by the query without a forecast is a pilot that does not get renewed.
A FinOps dashboard the CFO signs off on before the VP of Marketing signs on. Spend ceilings, anomaly alerts, per-use-case budgets, all visible from week one.
Onboarding scales slowly.
Solutions Architect time is the bottleneck. Every new vertical asks for the same first 90 days, from scratch. The work is repeatable. Most agencies are not set up to repeat it.
A repeatable pod playbook. One vertical at a time. Reference-ready in 90 days, with a co-sellable motion behind it.
We've shipped this for ourselves.
OneBenefits, 2025. The same architecture pattern we deploy for clients. End-to-end in eight weeks. Three numbers tell the story.
| What we called it | Where it lives in the stack |
|---|---|
| Persona Segmentation | Customer Intelligence Graph on a governed data lake. |
| Campaign Manager | Brief-to-Assets Agent inside the workspace. |
| Unified Scheduler | Next-Best Action coordination across web, email, chat. |
Four to six weeks. Not twelve plus.
We extend what's already in your environment. Most agencies start by rebuilding it. That delta is where the time goes.
| A typical agency | Innovative Group |
|---|---|
| Builds data pipelines from scratch | Taps your existing event stream. Live today. |
| Implements a new consent layer | Uses your existing OneTrust categories |
| Builds a content taxonomy from zero | Extends your existing content classifier |
| Trains a recommendation model from scratch | Adds a real-time serving layer on top of your existing batch model |
| Rebuilds analytics and observability | Hooks into MLflow and OTEL you already run |
The patents and frameworks behind the work.
Innovative Group's AI Solutions team is a builder. The patents protect the platform pieces we ship. The frameworks govern how we ship them.
4 patents
Scalable content intelligence and governance. The IP that lets the agent layer reason against enterprise content libraries without losing source attribution.
SAGE™
Scalable, governed agent architectures. Our reference pattern for agent design, evaluation, and deployment inside enterprise data platforms.
AETHER™
Enterprise AI transformation blueprint. The sequencing logic for moving an organisation from pilot scatter to production-grade AI delivery.
Three doors into the work.
Pick the shape that matches the room you're in. We can move on any of them inside two weeks.
Adaptive Platform Pilot
One vertical, one real signal-to-action loop, in your workspace. Scoped, fixed, shippable. First proof of value in 4 to 6 weeks. The fastest read on whether real-time NBA earns its keep on your stack.
Stack and Gaps Workshop
Two days, your data, our framework. We map your current state against the four-layer architecture and the four tensions. You leave with a sequenced plan and a build-vs-buy call on each layer.
Reference Architecture Review
A senior pod walks your platform, identifies the highest-leverage build, and produces a reference architecture you can hand to your engineering team. No commitment beyond the read-out.