Accelerate with AI

Guidance on your AI journey

Technical consulting

Expert-led technical design and review generative AI solutions. Leverage established design patterns for AI.

Rapid prototyping

Quickly assess AI-behavior with a Proof of Concept (POC). Use real-world data and feedback to make informed decisions.

AI safety review

Mitigate risks with an AI Safety Review. Actively defend against prompt injection, jail-breaking, and other AI risks.

Our expertise

Ramp Consulting guides clients through the complexities of building Generative AI solutions. We help teams address common challenges encountered when adopting Large Language Models (LLMs), such as navigating the intricate landscape of vector stores, RAG, prompt-chaining, orchestration, and AI prompt testing. Our expertise closes the knowledge gap and leverages leading practices.

Maximizing AI value

Unlocking the full value of Generative AI begins with tangible proof points. Successful teams showcase results of AI development early and often in the development cycle through prototypes, POCs, and incremental releases. Development agility and speed are essential to maximizing AI value — and operating nimbly in a competitive environment. Ramp Consulting helps teams deliver incremental wins faster.

Additionally, Ramp offers insights into implementing a structured "LLM Ops" framework to create a sustainable operational process that enhances existing DevOps practices. This approach ensures a reliable, repeatable, and seamless integration of AI into development lifecycle, maximizing efficiency and innovation.

Avoiding common AI pitfalls

Below are five reasons why AI projects can fail or take longer than expected to complete. Our goal is to help clients avoid these and other pitfalls - so that they can deliver rapidly and with confidence:

  1. QA Testing: Traditional software engineering involves deterministic code which reliably returns the same result given the same inputs. However, LLMs are non-deterministic and produce variant outputs making Generative AI more challenging to QA test.

  2. Learning curve: Solutions with LLMs use a different architectural approach compared to traditional software. Teams may experience a steep learning curve.

  3. Cost: Using LLMs can be expensive. However, techniques exist to reduce LLM resource costs - for example prompt caching, chat-history compaction, and purpose-fitting model to task.

  4. Scaling: Generative AI solutions require consideration for API key parallelism limits, data privacy, safety, and the hosting topography. Architecture decisions made early on matter later.

  5. AI safety: Organizations often lack the skillset to adequately protect LLM solutions from manipulation, attack, or misuse such as jailbreaking and prompt-injection.

Common uses of LLMs

The following are the most common ways LLMs are used. Typically a Generative AI solution consists of several of these uses combined to deliver a cohesive feature-set:

Conversational AI

Chatbots, assistants, plugins

Extrapolation and interpretation

Fraud detection, summarization, classification, sentiment analysis, opinion mining, persona extraction, translation, transcription

Sematic Routing & Planning

Intent analysis, sematic routing, orchestration, prompt chaining, planners

Content generation

Text completion, chat completion, image generation, code generation

Semantic memory & search

Semantic search, hybrid search, web search, text-embedding, long term memory (vector stores)

Agents

Agents, micro-agents, autonomous agents, co-pilots, function calling & procedural memory

How we work…

At Ramp Consulting, we tailor-fit engagements to meet your unique needs. Engagements can be narrow or broad in scope. We’re happy to be a sounding board; or partner with you for the long haul.

Above all, we aim to deliver immediate value through our deep expertise, actionable insights, and effective risk mitigation strategies.

Unsure if your project is a fit? We’re available to chat and can let you know how we can help.

We would love to hear what you’re building!

“The best way to predict the future is to create it.”

— Peter Drucker