Your next buyer brings an agent.
Discovery has moved into answer engines. Evaluation is moving into AI assistants that summarize reviews, compare specifications and shortlist vendors. Transactions are beginning to move into agents that can fill carts, request quotes and book meetings through APIs. Each step inserts a machine intermediary between your brand and your customer.
That changes what martech has to do. It must expose accurate, structured, permissioned data to those intermediaries; generate content at the speed and specificity they reward without sacrificing accuracy or brand integrity; and measure influence that increasingly happens inside interfaces you don't control and can't tag.
We help leadership teams make that transition deliberately — rationalizing the stack they already own, adding AI where it compounds value, and putting governance around it so speed doesn't come at the cost of trust, privacy or accuracy.
An AI-native stack, layer by layer.
Vendor-neutral by design. We architect around your existing platforms wherever possible and recommend replacement only when the economics are clear.
First-party data foundation
Unified customer profiles built from consented, server-side event streams. Deterministic and probabilistic identity resolution, consent propagation to every downstream tool, and warehouse-native activation through reverse ETL — so models are trained and prompted on data you own, govern and can explain. Clean-room collaboration where partner data adds lift without exposing PII.
AI-native content operations
Generation grounded in a curated brand knowledge base via retrieval-augmented generation, not in a model's general memory. Style and claims guardrails, automated evaluation harnesses for accuracy and tone, editorial review gates, and content provenance metadata — producing more specific content, faster, with an auditable chain from source fact to published sentence.
Agent-ready commerce
Machine-readable offers — pricing, availability, specifications, policies — exposed through structured data, merchant feeds and authenticated APIs that assistants and agents can query reliably. We evaluate emerging agent interoperability standards such as the Model Context Protocol and design the integration layer so your catalog and booking flows are ready when buyers delegate the transaction.
Intelligent orchestration
Propensity and churn models feeding next-best-action logic across CRM, marketing automation and paid media. Agentic workflows handle research, enrichment, routing and first-draft personalization under explicit permissions and approval thresholds, while humans retain authority over strategy, offers and anything customer-facing that carries risk.
Measurement for a zero-click world
When influence happens inside an AI answer, last-click attribution undercounts it. We combine media-mix modeling, geo and holdout incrementality tests, server-side conversion APIs, AI-referral segmentation and citation-share tracking into a triangulated view of what actually drives pipeline — reported in the language of finance, not vanity metrics.
Where is your organization on the curve?
Most companies sit between stages two and three: connected data, scattered AI pilots, no shared governance. The goal isn't to leap to stage five — it's to advance on purpose, one load-bearing capability at a time.
Siloed tools, client-side tags, manual reporting, no AI usage policy.
Central warehouse or CDP, consent managed, basic automation, ad-hoc generative AI use.
Predictive models in production, RAG-grounded content, governed prompts and evals, AEO measured.
Agentic workflows with approval thresholds; offers and inventory exposed through structured APIs.
Brand, catalog and service fully legible to external agents; continuous, triangulated measurement.
Speed with guardrails.
AI in marketing touches customer data, public claims and brand reputation simultaneously. Governance isn't a compliance appendix — it's what lets you scale usage without scaling risk.
Data privacy & consent
PII minimization, consent-aware pipelines and vendor data-processing reviews before any model sees customer data.
Accuracy controls
Grounding requirements, claim verification and automated evals that catch hallucinations before publication.
Brand safety
Voice, terminology and prohibited-claims policies encoded as system prompts, validators and review gates.
Agent permissions
Least-privilege tool access, spend and action limits, audit logging and human approval for consequential steps.
Model lifecycle
Versioned prompts, regression testing when vendors update models, and documented fallbacks.
Disclosure & provenance
Clear policies on AI-assisted content and provenance metadata aligned with emerging regulation.
A roadmap you can act on in a quarter.
Stack & readiness audit
Tool inventory and overlap, data-flow mapping, consent and identity review, AI usage census and AEO baseline.
Target state & quick wins
Target architecture, governance framework, and two or three high-value pilots — typically grounded content ops and server-side measurement.
Prove and productionize
Pilot results measured against baselines, playbooks documented, and a 12-month investment roadmap prioritized by impact.