Production RAG AI agents that execute growth workflows safely
Growthcurve develops RAG AI agents that connect your knowledge, CRM, and live APIs to autonomous workflows across marketing and revenue operations. We engineer hybrid retrieval, multi-agent orchestration, and guardrails so outputs stay grounded in your data, with observability to track accuracy, latency, and cost. You get working agents that your team can rely on: from creative and content generation to lead nurturing, support, and reporting. We integrate like internal staff, move faster than traditional teams, and keep you on a monthly rolling basis so delivery stays tied to outcomes.
Build agents that answer with evidence, not vibes
Standalone LLMs are impressive, but they often guess when they do not have the right context. RAG AI Agent Development fixes that by retrieving relevant facts from your sources before generation and action. We start by choosing the highest-impact use case (for example, lead qualification, creative iteration, or support deflection) and defining success and risk boundaries. Next we design the knowledge layer: what content is in scope, how it is chunked, and how it is updated. Then we build the agent workflow: intent detection, query planning, retrieval, tool calling, and a validation step that checks citations and confidence. Finally, we deploy with logging and access controls. Research shows RAG can reduce hallucinations by 73 percent and lift response accuracy to around 85 percent in support scenarios, which is exactly what you need before you scale automation.
Why this matters now
Retrieval that works in the real world (hybrid, reranked, updated)
The quality of your agent is limited by retrieval quality. We implement hybrid retrieval that combines dense embeddings with sparse keyword matching, then rerank results so the model sees the best evidence first. For long documents, we apply chunking and overlap windowing, often using overlap ratios around 20 percent to preserve context without inflating cost. For relational questions like account hierarchies, product bundles, or multi-touch journeys, we add multi-hop retrieval and can connect to knowledge graphs. We also build reindexing schedules and drift checks so agents do not degrade as your business changes. Where needed, we support multiple knowledge bases (for example, public marketing facts vs internal policies) with strict permissions. The outcome is fewer irrelevant answers, faster time-to-resolution, and agents that stay accurate as you ship new products and campaigns.
Agentic workflows: planning, tool calling, and reasoning loops
Traditional RAG is often a single request and response. Agentic RAG is a workflow: the system plans steps, calls tools, and iterates when it is unsure. We develop agents with query routing and query planning so each request hits the best source, whether that is a vector database, CRM, BI table, or external API. For complex tasks, we build multi-step execution chains: retrieve evidence, run calculations in a code interpreter, draft output, then self-evaluate. We include uncertainty quantification and set rejection behaviour, such as refusing to answer below a 0.7 confidence threshold and escalating to a human. When you need more throughput, we design multi-agent teams: one agent retrieves, another analyses sentiment or performance, and a third produces structured outputs like AIDA creatives or experiment briefs. The result is reliable automation for work that normally takes several people and several tools.
Where this fits
Full-funnel marketing agents, built for daily execution
RAG agents become most valuable when they are wired into the full funnel, not used as a novelty chatbot. For top-of-funnel, we connect social listening and news APIs so agents can generate SEO-focused content and social copy based on live trends while staying grounded in your positioning. For mid-funnel, we integrate CRM and behavioural signals to create personalised nurture sequences, ABM plays, and dynamic landing page copy that reflects what a lead has done and what they care about. For bottom-funnel, we build objection handling flows and comparison responses grounded in your docs, plus upsell and cross-sell recommendations based on transaction history. Tactics can include creative variant generation, audience segmentation suggestions, and autonomous campaign optimisation proposals. You get faster iteration across channels without relying on inconsistent freelancers or brittle manual processes.
What success looks like
Omnichannel support and client comms with measurable speed gains
Many teams start with support and internal enablement because the ROI is immediate and the data is clear. We build RAG agents that answer customer and client questions using your knowledge base, product docs, and policy libraries, with strict citation and escalation rules. We can deploy across web chat, mobile, and messaging, and use visual flow builders for hand-offs and routing. Agents can also summarise tickets, draft replies, and generate internal briefings for account teams. Research indicates RAG-powered agents can cut response times by around 60 percent for client queries, while improving accuracy versus standalone models. We also add analytics for deflection, satisfaction signals, and resolution quality. The outcome is a support experience that scales without sacrificing trust, and an internal team that spends less time searching and more time executing.
Security, access, and SLAs for production deployments
If an agent can access your data, it must be designed like a production system, not a demo. We implement least-privilege access, environment separation, and clear data boundaries between knowledge bases. We define what the agent is allowed to do (read-only, suggest, or execute) and add approval gates for high-impact actions like pricing changes, campaign launches, or CRM updates. We also build audit logs so you can trace what was retrieved, what was generated, and which tools were called. For enterprise volumes, we design for throughput and reliability, including rate limiting, caching, fallbacks, and error handling. We agree operational expectations up front: response times, on-call coverage where needed, and a clear owner for decisions. You get enterprise-grade security and predictable delivery without a long-term contract.
Observability and iteration: how the agents get better each week
An agent that is not monitored will drift. We ship with observability that tracks system health and business outcomes, so improvement is continuous rather than accidental. On the system side we monitor latency, cost per query, retrieval hit rate, reranker performance, and grounding or faithfulness checks. On the business side we tie agent work to funnel movement and efficiency: content output and engagement for TOFU, MQL to SQL progression for MOFU, and conversion, ROAS, CAC and LTV signals for BOFU and retention. We run an evidence-led cadence: weekly review of failures, prompt and routing tweaks, corpus updates, and new experiments. Where appropriate, we use interaction feedback to adapt retrieval strategies over time, improving satisfaction and reducing manual rework. You get a real-time dashboard and a roadmap that is driven by results, not opinions.