Artificial Intelligence

LLM Consulting & Solutions

Independent LLM strategy, evaluation, and deployment engineering that turns promising pilots into reliable, production-grade AI features.

Discuss this capabilityBack to AI
  • Model-agnostic evaluations
  • Guardrails before go-live
  • Senior AI engineers only
Overview

LLM systems built for production, not demos

Large language models are easy to prototype and hard to productionise. A slick demo can fall apart once it meets real users, messy data, and edge cases that were never in the test set. We help engineering and product teams close that gap — choosing the right model and architecture for the task, building evaluation harnesses that catch regressions before users do, and designing guardrails that keep outputs accurate, on-brand, and safe to ship.

Our consultants work across the stack — from prompt and retrieval design to fine-tuning decisions and inference cost — so you are not locked into a single vendor's roadmap. We benchmark providers like OpenAI, Anthropic Claude, and open-weight models against your own tasks, then build the evaluation and monitoring infrastructure that lets you ship changes with confidence. The result is an LLM layer your team understands, can maintain, and can defend to auditors, security teams, and customers.

Capability focus

  • LLMs
  • Evaluation
  • Guardrails
  • Fine-tuning
  • Discovery workshops
  • Architecture & documentation
  • Post-launch support
Offerings

What we deliver

Consulting and engineering across the full LLM lifecycle — from model choice to production monitoring.

Model Selection & Benchmarking

Structured evaluation of proprietary and open-weight models against your real tasks and data, so you choose on evidence — cost, latency, and quality — not vendor marketing.

Prompt & Retrieval Engineering

Prompt systems, context design, and RAG pipelines tuned to your domain — grounding responses in your own documents, policies, and data instead of model memory alone.

Fine-Tuning & Adaptation

Where prompting and retrieval are not enough, we scope, run, and evaluate fine-tuning jobs — and are equally direct when a lighter approach will do the job.

Evaluation Harnesses & Guardrails

Automated test suites, structured red-teaming, and output guardrails that catch hallucination, prompt injection, and policy violations before they ever reach a customer or an auditor's desk.

Inference Cost & Performance Tuning

Caching, request routing, batching, and model right-sizing that cut token spend and latency in production, without degrading the response quality your users actually notice day to day.

Governance & Team Enablement

Usage policies, review workflows, and hands-on training so your own engineers can extend, monitor, and safely evolve the LLM systems we help you design and build.

Process

How an LLM engagement runs

A structured path from problem definition to a monitored production system.

  1. Discovery & Feasibility

    We map the use case, data sources, and success metrics, then assess honestly whether an LLM is the right tool before any build begins.

  2. Architecture & Model Selection

    Benchmarking candidate models and designing the prompt, retrieval, and fallback architecture that will carry the feature into production.

  3. Build & Evaluate

    Iterative build with an evaluation harness running on every change, so regressions are caught before a release, not after.

  4. Deploy & Monitor

    Production rollout with guardrails, logging, and cost dashboards, plus a plan for ongoing evaluation as models and usage evolve.

Why Ramest

Why teams trust Ramest with LLM work

Vendor-neutral advice

We are not tied to a single model provider, so recommendations are based on your evaluation results and constraints, not a partnership agreement or reseller margin.

Evidence over intuition

Every model, prompt, and fine-tuning decision is backed by a measurable evaluation run against your own tasks, not a general leaderboard score or a demo that looked good once.

Safety built in, not bolted on

Guardrails, red-teaming, and monitoring are part of the build from day one, so accuracy and safety issues surface in testing rather than in front of a customer.

A small senior team

You work directly with the engineers doing the evaluation and build work — no account layer, no junior hand-offs, and a team small enough to move fast.

Stack

Tools and models we work with

A model-agnostic toolkit spanning proprietary APIs, open-weight models, and evaluation infrastructure.

Models & APIs
  • OpenAI
  • Anthropic Claude
  • AWS Bedrock
  • Llama
  • Mistral
Orchestration & Retrieval
  • LangChain
  • LangGraph
  • LlamaIndex
  • Pinecone
  • pgvector
Evaluation & Monitoring
  • Weights & Biases
  • LangSmith
  • Ragas
  • PromptLayer
Infrastructure
  • Python
  • Docker
  • AWS
  • PostgreSQL
FAQ

Frequently asked questions

What technical and business leaders ask before starting an LLM engagement.

How much does LLM consulting cost?

Cost depends on scope — how many use cases you need covered, how ready your data is, and how much evaluation and guardrail work your risk profile demands. We scope every engagement through a consultation call, then agree a fixed quote before work begins, delivered as a fixed-scope project or a dedicated team. We build for businesses of every size, from a single scoped feature such as a RAG assistant to a larger multi-model programme, so a smaller, focused engagement is just as welcome as an enterprise one.

How long does an LLM consulting engagement take?

A focused engagement — model selection, a working prototype, and an initial evaluation harness — typically takes 4–8 weeks. Larger programmes covering fine-tuning, multi-model routing, and full guardrail and monitoring infrastructure run 10–16 weeks, usually delivered in phases so you have a working feature in production well before the roadmap is complete.

What does LLM consulting actually involve?

LLM consulting is the work of turning a large language model into a reliable product feature — choosing the right model, designing prompts and retrieval, and building the evaluation and guardrail systems that keep outputs accurate and safe. It sits between AI strategy and hands-on engineering, and typically ends with a production-ready implementation, not just a recommendation document.

Do we need fine-tuning, or is retrieval-augmented generation (RAG) enough?

Most business use cases are solved with RAG and good prompt engineering alone — fine-tuning is the exception, not the default. We recommend fine-tuning only when a task needs a consistent style, format, or domain behaviour that retrieval and prompting cannot reliably produce, and we always test the cheaper option first before proposing the more expensive one.

How do you prevent hallucination and inaccurate answers in production?

We reduce hallucination through grounding — retrieval over your verified data, structured prompts, and citations back to source documents — combined with an evaluation harness that scores accuracy on real examples before every release. Guardrails add a final check, catching unsupported claims or out-of-policy responses and routing uncertain cases to a human instead of guessing.

What happens after the LLM system is deployed?

You own the code, prompts, evaluation datasets, and infrastructure configuration outright. We typically stay engaged through a support retainer covering monitoring, model updates, and evaluation re-runs as providers release new versions, since an LLM feature that is not re-tested against model updates tends to drift in quality over time.

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