The LLM agency.The right model, made reliable.
A model-neutral LLM agency: we pick the right model across Claude, GPT, Gemini and open weights, wire it into your product and ops, and make it reliable instead of leaving you a demo that worked once. RAG grounded on your data, agents with tool calling, evals to compare models, and no vendor lock-in.
★★★★★Verified Trustpilot reviews · AI, automation & growth agency
ActiveCampaign
Adalo
AdCreative.ai
Ahref
Airtable
Allo (The Mobile First Company)
Anthropic
Apify
Apollo.io
AttioAttio Implementation Partner
Base44
Baserow
Brevo
Bright Data
Browse AI
Bubble
CaptainData
ChatGPT
Claude
Claude CodeClaude CoworkClaude Design
Clay
Clickup
Cursor
DeepSeek
Dust
ElevenLabs
Fillout
Flutterflow
Folk CRM
Folk Implementation Partner
Freepik Spaces
Gamma
GeminiA model-neutral LLM agency picks the right model, not the most hyped.
Anyone can call an API. Comparing models on your real data, integrating them without lock-in, and proving quality with evals is a different job. Here are the four things we own.
- Multi-model integration
The right model, wired into your product and ops
Picking an LLM isn't checking a brand box and locking yourself in. We wire the right model into the apps and workflows your business actually runs on: support, search, document processing, internal copilots. A RAG pipeline grounded in your data, function and tool calling to your real systems, and a layer that routes each task to Claude, GPT, Gemini or an open-weights model based on what performs best. You swap models without rewriting the product.
See a typical build - AI agents, model of your choice
Agents that do the work, whatever model sits behind them
The leverage isn't a chatbot, it's agents that own a task end to end with tools and memory. We build them for the work that eats your team's week: ticket triage, data extraction, research, multi-step ops. Each one is scoped, has only the tools and permissions it needs, and ships with a human review step. Because the orchestration is decoupled from the model, you move an agent from GPT to Claude or to a smaller model without breaking everything.
See the method - Evals & model comparison
A model choice you can measure, not a bet on a public benchmark
The top model on a public leaderboard isn't necessarily the best on your data. We build evals that compare Claude, GPT, Gemini and open weights on your real cases, before and after every change. We add guardrails for hallucination control and unsafe output, and wire observability so you can see what the model does in production. Cost and latency are tuned on purpose: the right model per task, caching, and prompts that don't burn tokens for no reason.
See the integrations - Enablement & ops
Your team drives the model, without depending on us
A multi-model setup nobody on your side can maintain is a liability. We document the routing logic, the prompts, the evals and the guardrails, and train your team to swap models, trade cost against quality, and extend the whole thing. We're an automation and AI agency first, working with US teams and remote, so the LLM work plugs into how your business already operates instead of sitting in a side project.
See AI enablement
We choose the model like engineering, not by reflex.
Most LLM projects marry a model at the first prototype, then find out too late that it's expensive, regresses, or doesn't fit their data. So we treat it like engineering: we benchmark the models on your cases, integrate the right one with no lock-in, measure with evals, fence it with guardrails, then hand it to a team that can swap models on its own.
- Audit · map your use cases and where an LLM genuinely adds value, and where it doesn't
- Selection · benchmark the models on your data and pick the right one per task, cost included
- Build · integrate multi-model with RAG, agents, evals and guardrails, no vendor lock-in
- Enable · document the routing and evals, train your team so they swap models on their own
We're model-neutral, genuinely.
We don't sell a partner tier. We build real software with LLMs, including this site, so we choose models the way they actually hold up: compared on your data, integrated without lock-in, measured with evals, and tuned for cost and latency. That's exactly what's missing when an LLM project marries the model of the moment and gets stuck six months later.
- We test the models on your real data, not on a public benchmark, so we pick the right one per task instead of selling you the model of the month.
- Model-neutral, genuinely: Claude, GPT, Gemini, Mistral, Llama, DeepSeek or Grok, we choose on fit and cost, not on a partner tier we're paid to push.
- Zero lock-in: the integration is decoupled from the provider, so you swap models without rewriting your product when prices or quality shift.
- You leave autonomous: routing, prompts, evals and guardrails are documented in your repo, so your team trades cost against quality without us.
The right model at the core, the reliable system around it.
We build the parts that turn a large language model into dependable throughput, then connect them to how your business already runs, without tying you to one provider. Here's what a real LLM build covers.
- Setup
Model selection & benchmarking
We compare Claude, GPT, Gemini, Mistral, Llama, DeepSeek and Grok on your real cases, not on a public leaderboard, to pick the right model per task across frontier, open weights and self-hosted.
- Setup
Multi-model integration & routing
We put an abstraction layer that routes each task to the right model and lets you swap without rewriting the product, so you're never locked into a single provider.
- Setup
RAG pipelines
We build the retrieval-augmented generation pipeline that grounds the model in your data: chunking, embeddings, a vector DB, and retrieval tuned so answers cite your sources instead of making things up.
- Setup
AI agents & tool calling
We build agents with function and tool calling wired to your real systems, scoped permissions, and memory, so they complete multi-step tasks instead of returning a paragraph you still have to act on.
- Setup
Evals & guardrails
We build evals to measure quality on your real cases and guardrails for hallucination control and unsafe output, so a prompt change or a model swap can't silently regress your feature.
- Setup
Cost, latency & observability
We ship behind an API with logging, tracing and cost dashboards, and optimize the model mix so you can see what runs in production, catch drift, and keep the token bill predictable.
We map where an LLM fits, you leave with a plan.
Before quoting anything, we take 60 minutes to look at your use cases, your data and your stack. You leave with an honest read on where a large language model genuinely helps, which model to target, and what to keep as plain code. Zero pitch, just an engineer's take on your problem.
- An honest read on where an LLM actually helps
- Which model to target across Claude, GPT, Gemini and open weights
- The RAG, agents or evals worth building first
- A frank take on what it won't fix
How we run an LLM build.
Five steps, in order. We don't pick a model without benchmarking it on your data, we don't ship a feature before the evals exist, and your team owns it at the end. Each step has a deliverable and you sign off before we move on.
- Step 1 · Use-case audit
Find where an LLM genuinely adds value
We sit down with your team and look at the real work: support volume, documents nobody has time to read, search that doesn't find anything, repetitive ops. We check your data and your stack. Half the value is telling you which cases an LLM fits and which ones are cheaper and safer as plain code, so you don't ship a large language model against a problem it won't fix.
- Step 2 · Model selection & benchmarking
Choose the right model on your data, not on a leaderboard
A model that tops a public benchmark can flop on your cases. We test Claude, GPT, Gemini and open weights like Mistral, Llama or DeepSeek on a sample of your real work, measure quality, cost and latency, and decide which model per task. Quality depends on your data, so we're honest early about what your sources can and can't support, and what to clean up first.
- Step 3 · Multi-model build with evals
Ship the feature with quality you can measure
We build the RAG pipeline or the agents, wire function calling to your systems, and add a routing layer that sends each task to the right model. Evals run from day one so quality is measured, not guessed. Guardrails handle hallucination control and unsafe output, and cost and latency are tuned on purpose. A human stays in the loop on anything that matters.
- Step 4 · Deploy & integrate
Put it in your product and your stack
We deploy the feature behind an API and wire it into the apps and workflows your business runs on, with logging, tracing and cost dashboards from the start. The multi-model routing lives there, so switching models doesn't need a heavy redeploy. You see drift, cost and quality at a glance instead of finding out from a complaint.
- Step 5 · Enable & hand over
Train the team, then get out of the way
We document the routing, the prompts, the evals, the guardrails and the model choices, and train your team to run, swap models and extend the feature. If you want to go deeper, our AI training covers RAG, agents and the SDK end to end. You leave able to trade cost against quality and switch providers without us.
We're judged on the features that ship.
No partner badge to display, so we lead with what matters: feedback from the teams whose LLM features we built, and whether those features still held up after we left, even after they switched models. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The routing, prompts and evals live in your repo, owned by your team
- Models compared on your data before anything reaches a user
- Agents scoped, fenced with guardrails, kept human-in-the-loop
- Trustpilot reviews come from the teams we built features for
The questions we get asked on repeat.
What does an LLM agency actually do?
An LLM agency picks the right language model for your case and integrates it into your product and operations reliably, instead of leaving you a demo that impressed once. We benchmark Claude, GPT, Gemini and open weights on your real data, design and build RAG pipelines, AI agents with function and tool calling, embeddings and vector DB setup, evals to measure quality, and guardrails for hallucination control. We ship behind an API your team owns, with a routing layer that lets you swap models without rewriting the product. The point is a dependable feature in production, not a prototype nobody trusts.How do you choose between Claude, GPT, Gemini and open weights?
It's decided on your data, not on a public leaderboard. We take a sample of your real work and compare models on three axes: quality, cost and latency. For heavy reasoning or code, a frontier model like Claude or GPT is often worth it. For high-volume or cost-sensitive cases, a smaller or open-weights model self-hosted (Mistral, Llama, DeepSeek) is the better call, and Gemini fits others. We build evals so you decide on numbers of your own, not on a marketing benchmark.What is multi-model LLM integration?
It's an architecture where your product isn't married to a single provider. We put an abstraction layer that routes each task to the best-suited model: the frontier one where you need precision, a cheaper one where volume matters. In practice, that means you can switch from GPT to Claude, or test an open-weights model, without rewriting your feature. It's what protects you when prices move, a model regresses, or a better one ships the next month. The routing, prompts and evals stay documented in your repo.Are we locked into a single provider?
No, that's the whole point. Vendor lock-in is the real risk of an LLM project: you build everything around one model, its prices climb or its quality drops, and migrating costs a rewrite. We design the integration decoupled from the model from the start, with a routing layer and versioned prompts. You can compare Claude, GPT, Gemini and open weights on your evals and switch on fit and cost. We have no partner tier to push, so we have no reason to tie you to one provider over another.How much does an LLM project cost?
It depends on scope: a single RAG feature is nothing like building several agents wired into your systems with evals and observability. We don't throw out a flat package. We start with a free 60-minute audit to find where an LLM genuinely helps, then quote a fixed scope. The model usage itself you pay the provider (Anthropic, OpenAI, Google) directly, or you self-host open weights; we design model selection and caching so the token bill stays predictable instead of surprising you.When is an LLM the wrong tool for the job?
More often than the hype admits, and we'll say so. If the task is a clear rule, a lookup, or a calculation, deterministic code is cheaper, faster and safer than a large language model, and it won't hallucinate. LLMs earn their place on language, ambiguity and unstructured data: support, search, document processing, drafting. Part of the audit is drawing that line honestly, so you don't pay frontier-model prices for work a simple script does better.What is RAG and do we need it?
RAG (retrieval-augmented generation) grounds the model in your own data: instead of answering from training alone, it retrieves the relevant documents from a vector DB and answers from them, which cuts hallucinations and lets it cite sources. For most business cases (support, internal search, document Q&A) RAG is the right architecture before you ever consider fine-tuning. We build the chunking, embeddings and retrieval, and tune it so the answers are grounded, not invented. RAG works with any model, which keeps your choice open.Can you build AI agents, not just a chatbot?
Yes, that's where the leverage is. A chatbot answers; an agent acts. We build agents with function and tool calling wired to your real systems, scoped permissions and memory, so they complete multi-step work: ticket triage, data extraction, research, ops. Each agent is scoped to a task, gets only the tools it needs, and ships with a review step so a human approves anything that matters. Because the orchestration is decoupled from the model, you can run the same agent on Claude, GPT or a smaller model depending on cost.Do you work with US teams or remote?
Both. We work with US companies and remote teams, and most of an LLM build runs perfectly well remote. A typical project starts with the 60-minute audit on your use cases, then we move in stages with a deliverable each time. Whether you're a US startup or a distributed product team, what matters is access to your data and your stack, not geography. We document everything in your repo so your team can take over, on-site or remote.
Stop marrying the model of the moment. Pick the right one.
A 60-minute audit, your use cases mapped, a build plan with the right model, the evals and the guardrails baked in. If your team can run it in-house after we build it, we'll hand you the playbook. If we're the right fit, we handle it.