AI & LLM Platforms
Most AI implementations produce demos,
not operating infrastructure.
We build AI agents, LLM workflows, and automation layers on top of the platforms your team already pays for - connecting models to real data, real systems, and real revenue processes.
Book a strategy call$250M+
Pipeline generated
42%
Average pipeline growth
18.3%
Average budget saved
Results from actual client engagements.
Your AI spend is growing but your operations are not changing.
Most mid-market firms have already purchased access to at least one LLM platform - OpenAI, Anthropic, Google Gemini, Azure OpenAI, or a vertical-specific model. The licenses exist. The enthusiasm existed at the kickoff meeting. What does not exist is a working connection between the model and the actual systems where revenue work happens: the CRM, the ERP, the support queue, the proposal workflow. The result is a collection of browser tabs where individual reps paste things manually, a few internal GPT wrappers that nobody adopted, and a growing sense that the technology is not delivering what the vendor promised.
The failure is almost never the model. It is the architecture around it. LLMs need clean context to produce useful output - which means structured retrieval, grounded data sources, and prompts engineered for your specific workflow, not a generic chatbot sitting on top of a disconnected knowledge base. Without that foundation, you get hallucinations on customer-facing content, agents that stall on edge cases, and outputs that require as much human review as the original manual process. Building that foundation is an engineering and operations problem, not a prompt-tweaking problem, and most internal teams are not staffed to solve it while also running the business.
The AI & LLM Platforms platforms we specialize in
Pick your platform. We'll make it deliver.
Anthropic Claude
We design and build Claude-powered agents, document processors, and internal tools that connect to your real data and actually run in production - not just in a demo.
Explore Anthropic ClaudeAWS Bedrock
We design and build production-grade AI workloads on AWS Bedrock - model selection, Knowledge Bases, Agents, and guardrails - so your team ships something that actually runs in operations, not just in a demo.
Explore AWS BedrockAzure OpenAI
We architect and deploy Azure OpenAI - GPT-4o, embeddings, Assistants API, and RAG pipelines - inside your existing Azure tenant, connected to your real data and workflows, not a sandbox.
Explore Azure OpenAIGoogle Vertex AI
Revenue Institute designs and deploys Vertex AI agents, RAG pipelines, and fine-tuned Gemini models that connect to your real data, your real workflows, and the systems your team already uses.
Explore Google Vertex AIOpenAI
We design and build OpenAI-powered agent and API workflows that connect to your CRM, your data, and your real processes - so the model does useful work instead of sitting in a sandbox.
Explore OpenAIWhy mid-market firms bring us in for AI implementation.
Agent design that connects to live systems
We build AI agents that read from and write to your actual stack - CRM records, deal data, support tickets, ERP line items. That means the agent has the context it needs to do something useful, and the output lands where your team already works, rather than in a separate interface that nobody checks.
Prompt architecture built for your workflows
Generic prompts produce generic output. We design prompt templates, system instructions, and retrieval logic around your specific processes - sales qualification, contract review, renewal forecasting, whatever the use case is. The goal is outputs your team can act on without a second pass.
RAG and retrieval layer setup
Retrieval-augmented generation is how you give a model accurate, current, company-specific knowledge without retraining it. We build and maintain the retrieval layer - chunking strategy, embedding model selection, vector store configuration - so the model answers from your actual data, not its training cutoff.
Model selection without vendor bias
We are not reselling any model's API. We evaluate OpenAI, Anthropic, Gemini, Mistral, and open-source options against your latency requirements, cost tolerance, data privacy constraints, and output quality needs for the specific task. The right model depends on the job, and the answer changes as the market moves.
Rescue of stalled or broken AI projects
If a previous build produced something that does not work in production - agents that hallucinate, pipelines that break on real data, automations that required more maintenance than the manual process - we diagnose the architecture, identify what is salvageable, and rebuild the parts that are not.
Governance and output quality controls
Deploying an LLM into a customer-facing or revenue-critical workflow without guardrails is an operational risk. We implement evaluation frameworks, confidence thresholds, human-in-the-loop checkpoints, and logging so you know when the model is performing and when it needs intervention.
What mid-market AI implementation actually requires
The gap between a working AI demo and a working AI system is almost entirely an infrastructure problem. Large language models are capable of producing genuinely useful output on revenue tasks - summarizing call transcripts, drafting renewal outreach, flagging at-risk accounts, extracting structured data from unstructured documents. The capability exists. What most mid-market implementations are missing is the data pipeline that gives the model accurate context, the integration layer that connects output to the systems where work happens, and the evaluation layer that tells you when the model is performing and when it is not.
Retrieval-augmented generation is the architecture that makes LLMs useful on company-specific tasks without retraining. Instead of relying on the model's training data, RAG pulls relevant documents, records, or data points at inference time and includes them in the prompt. That requires decisions about chunking strategy, embedding models, vector store selection, and retrieval ranking - none of which are configured by default when you purchase an API key. Getting this layer right is the difference between a model that answers from your actual product documentation and one that confidently invents specifications.
Agent workflows add another layer of complexity. An agent that can look up a CRM record, draft a follow-up email, and log the activity back to the deal requires tool definitions, error handling, state management, and guardrails on what the agent is permitted to do autonomously versus what requires a human decision. The frameworks for building this - LangChain, LlamaIndex, OpenAI Assistants, Microsoft AutoGen, and others - are mature enough to use in production, but they require engineering judgment about which abstraction fits the use case and where the framework's defaults will cause problems at scale.
Where AI fits in a mid-market revenue operation
The highest-value AI applications in mid-market firms tend to cluster around three areas. First, information synthesis: pulling structured insight out of unstructured sources like call recordings, support tickets, contracts, and email threads. Second, workflow acceleration: generating first drafts of proposals, renewal summaries, or qualification notes that a human reviews and sends rather than writes from scratch. Third, signal detection: monitoring CRM data, usage data, or financial data to surface accounts that need attention before a human would notice the pattern.
Each of these requires a different architecture. Synthesis tasks need strong retrieval and document processing pipelines. Acceleration tasks need prompt engineering tuned to your voice, your product, and your buyer. Signal detection needs structured data access, threshold logic, and a delivery mechanism that puts the alert in front of the right person at the right time. Treating all three as the same problem - drop in a chatbot and see what happens - is why most early AI projects in mid-market firms produced interesting demos and no operational change.
The firms that are getting durable value from AI investment are the ones that treated it as a systems integration project with a model in the middle, not a software purchase that would configure itself. That means scoping use cases by operational impact, building the data and integration infrastructure before worrying about model selection, and measuring output quality with the same rigor applied to any other operational process. That is the work we do.
AI & LLM Platforms questions, answered
Which AI or LLM platform should we use?
It depends on the task, your data environment, and your cost and latency tolerances. OpenAI's current models are strong general-purpose choices with broad tooling support. Anthropic's Claude tends to perform well on long-context and instruction-following tasks. Google Gemini has tight integration with Workspace. Open-source models like Llama or Mistral make sense when data cannot leave your infrastructure. We evaluate options against your specific use case rather than defaulting to the most-marketed name.
What is a realistic timeline to get an AI agent into production?
A focused, well-scoped agent - one that handles a single workflow like lead research, call summary generation, or renewal risk flagging - can reach production in four to eight weeks if the underlying data is accessible and reasonably clean. Multi-step agents that touch several systems, require fine-tuning, or need significant data cleanup take longer. The variable that most often extends timelines is data access, not model complexity.
We already have a ChatGPT Enterprise or Copilot license. Do we need a separate implementation?
Those licenses give you access to a capable model, but they do not configure the retrieval layer, the system prompts, the integration with your CRM or ERP, or the governance controls. Most firms with enterprise AI licenses are using a fraction of what the platform can do because the configuration work was never completed. We pick up where the vendor onboarding stopped.
How do we prevent the model from hallucinating on customer-facing content?
Hallucination is reduced - not eliminated - through grounding. That means retrieval-augmented generation that pulls from verified sources, prompt instructions that tell the model to cite or decline rather than guess, output validation steps, and human review checkpoints for high-stakes content. We design the workflow so the model's confidence level determines how much human oversight is required before output reaches a customer.
Can you build on top of our existing HubSpot, Salesforce, or other CRM?
Yes. Most of the AI workflows that matter in a mid-market revenue operation are connected to CRM data - contact enrichment, deal summaries, next-step recommendations, forecast commentary. We build the integration layer between the LLM and your CRM so the agent reads current record data and writes structured output back into the right fields, rather than operating in a disconnected side tool.
What does it cost to run an LLM in production at our scale?
API costs vary significantly by model, token volume, and whether you use hosted endpoints or self-hosted infrastructure. A well-architected implementation controls cost through caching, prompt compression, model tiering (using cheaper models for simpler tasks), and batching where latency allows. We build cost monitoring into every production deployment so you are not surprised by an API bill at the end of the month.
Do we need a dedicated data science team to maintain this after you build it?
Not typically. The workflows we build are designed for operational teams to monitor and adjust - not PhD-level maintenance. We document the prompt logic, set up logging dashboards, and train whoever owns the system on how to identify drift and when to escalate. For ongoing optimization, many clients use a retainer rather than hiring internally.
Not sure which AI & LLM Platforms platform fits?
We're vendor-agnostic. Tell us your goals and we'll recommend the right stack - then build it.
Book a strategy call