Build agentic RAG that drives measurable full-funnel growth
Growthcurve designs RAG AI agent strategies that connect your data to LLM workflows you can trust and measure. We combine retrieval-augmented generation with multi-agent orchestration so outputs are grounded in your CRM, content, and performance signals, not guesswork. You get a clear roadmap, a working prototype, and an implementation plan that fits your stack and team. The result is faster campaign iteration, more consistent personalisation, and safer automation across the funnel.
From scattered data to dependable marketing decisions
Most AI marketing programmes fail for one simple reason: the model does not know your business, so it improvises. RAG changes that by retrieving the right facts from your sources before generation, and agentic RAG takes it further by assigning specialised agents to plan, fetch, validate, and execute tasks. We start by mapping your highest-value use cases across awareness to retention. Next we audit data sources (CRM, CMS, product analytics, support, ad accounts) and define what is safe to use. We then design the retrieval layer (vector store, hybrid search, reranking) and the agent workflow (routing, tool calling, memory). Finally, we define governance and handover. You get grounded outputs, faster cycle times, and a system your team can run with confidence.
Why this matters now
Top-of-funnel: real-time creative that stays on-brand
At the top of the funnel, speed and relevance win, but generic AI copy risks brand drift and weak differentiation. With RAG AI agents, we pull from brand guidelines, positioning, approved claims, seasonality calendars, competitor benchmarks, and live social signals to create and refresh creative variants continuously. Practically, we set up retrieval over your brand and product knowledge, then connect listening and scraping sources to feed trend prompts. Agents generate multiple ad angles, hooks, and landing page variants, then propose experiments aligned to your audience hypotheses. Where appropriate, we use multimodal RAG to learn from winning visuals and translate them into new concepts. Research shows this approach can lift click-through rates by 25 to 35 percent through real-time iteration, while keeping messaging consistent.
Mid-funnel: agent swarms that nurture and qualify leads
Mid-funnel performance often stalls because teams cannot tailor follow-up at scale, and hand-offs between marketing and sales are messy. Agentic RAG makes lead nurturing more specific and timely by querying interaction history, intent signals, and CRM fields to decide what to say next and where to say it. We design a multi-agent system where one agent retrieves the right context (webinars watched, pages viewed, objections raised), another writes channel-specific messages, and a third checks compliance and tone. The system can orchestrate sequenced journeys across email, SMS, and retargeting, with propensity scoring to prioritise effort. We also build playbooks for common paths like pricing-page visits or competitor comparisons. Done well, this can improve pipeline velocity by around 20 percent by reducing lag and increasing relevance.
Where this fits
Bottom-funnel: just-in-time offers and conversion optimisation
When buyers are close to purchase, the wrong incentive or timing can cost you the deal. With RAG AI agents, we analyse cart abandonment patterns, purchase intent signals, and micro-segment behaviours to trigger interventions that are specific to the user and consistent with margin rules. We start by defining guardrails: approved offer types, pricing constraints, and escalation rules to humans. Then we connect product analytics, commerce events, and CRM opportunity stages into the retrieval layer. Agents decide on the best next action: an offer, a sales prompt, a help message, or a retargeting sequence. For experimentation, we can use multi-armed bandit approaches to allocate traffic as results emerge. Research indicates these workflows can lift conversions by 15 to 30 percent, especially where intent signals are strong.
What success looks like
Retention and expansion: lifecycle agents that learn over time
Retention is where the economics of growth are won, but it is hard to personalise lifecycle marketing without heavy manual work. Agentic RAG enables post-purchase agents to monitor NPS feedback, sentiment, support topics, usage patterns, and key lifecycle events, then recommend actions that protect revenue. We design retrieval over customer context and policy documents, and build agents that can draft win-back sequences, loyalty escalations, and product education campaigns tailored to segment and stage. Where appropriate, we incorporate reinforcement learning from human feedback so the system improves with review cycles, rather than drifting. We also add memory augmentation so important customer details persist safely across interactions. The outcome is more consistent customer communication, faster response to churn signals, and a lifecycle engine that improves with each iteration.
Measurement, observability, and continuous iteration
AI automation without measurement is just faster chaos. We set clear full-funnel metrics and observability so you can see what the agents are doing, why they chose an action, and what it cost. We define TOFU metrics like impressions and engagement quality, MOFU progression from MQL to SQL, and BOFU outcomes such as ROAS, CAC, and LTV:CAC trends. On the AI side, we track latency, retrieval hit rate, faithfulness scores, and cost per query, plus drift indicators that signal when the knowledge base is getting stale. We run a weekly optimisation cadence: review experiments, update retrieval sources, refine prompts and tools, and prune failing agent behaviours. You get a real-time performance dashboard and an evidence-led loop that reduces marketing guesswork.
How we deliver: clear ownership, secure access, rapid sprints
RAG AI agent strategy touches data, creative, and operations, so delivery needs structure. We integrate like your internal team and agree working rhythms and SLAs upfront: response times, review windows, and decision owners. In week one we run a discovery and architecture sprint, then deliver a prioritised roadmap and a thin-slice prototype that proves retrieval quality. Next we harden the pipeline: permissions, data contracts, indexing schedules, and tool calling policies. We then expand use cases across channels with a release cadence that your team can follow, including QA checks and rollback plans. You can scale the specialist team up or down as needs change, on a monthly rolling basis. The result is faster progress than in-house builds, without long-term lock-in.