Hack'celeration Agency · LLM 2026The LLM agencythat integrates models, builds agents, ships RAG, runs the evals, controls the costreliable AI, not a demo.
An LLM agency that integrates large language models into your product and operations and makes them reliable, instead of leaving you with a demo that worked once. We design the RAG pipeline, build AI agents with function and tool calling, pick the right model across Claude, GPT, Gemini and open weights, and ship it with the evals, guardrails and cost control that keep a clever prototype from breaking the day real users touch it.
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GeminiAn LLM agency ships reliable features, not a clever demo.
Anyone can call an API. Grounding a model in your data, building agents that actually act, and proving quality with evals is a different job. Here are the four things we own.
- LLM integration
Large language models wired into your product and ops
A demo in a chat window is not a feature. We integrate LLMs into the apps and workflows your business actually runs on: support, search, document processing, internal copilots. We design the RAG pipeline, wire function and tool calling to your real systems, set up embeddings and a vector DB on your data, and ship it behind an API your team controls. The model becomes a reliable part of the product, not a toy.
See a typical build - AI agents
Agents that do the work, not just answer questions
The leverage isn't a chatbot, it's agents that own a task end to end with tools and memory. We build AI agents 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 review step so it does the repetitive 80% while your people keep the judgment calls. Function calling and context engineering do the heavy lifting.
See the method - Evals & guardrails
Reliability you can measure, not vibes from a demo
An LLM feature that looks good once and breaks in production is worse than none. We build evals so you can measure quality before and after every change, add guardrails for hallucination control and unsafe output, and wire observability so you can see what the model does in the wild. Cost and latency are optimized 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 owns it, without depending on us
A clever LLM feature nobody on your side can maintain is a liability. We pick the model that fits (Claude, GPT, Gemini, or open weights), document the prompts, evals and guardrails, and train your team to run and extend it. We're an automation and AI agency first, so the LLM work plugs into how your business already operates instead of sitting in a side project.
See AI enablement
We ship LLM features like engineering, not a science fair.
Most LLM projects die the same way: a slick demo, no evals, no guardrails, and the first wrong answer in production kills the trust. So we treat it like engineering: grounded in your data with RAG, measured with evals, fenced with guardrails, and tuned for cost, then handed to a team that can run it.
- Audit · map your use cases and where an LLM genuinely adds value, and where it doesn't
- Design · RAG, agents, model selection, evals and guardrails scoped before any code
- Build · ship the feature with tool calling, observability and cost control baked in
- Enable · document prompts and evals, train your team so they own and extend it
We ship LLM features every day.
We don't sell a partner tier. We build real software with LLMs, including this site, so we design them the way they actually hold up: grounded in data, measured with evals, fenced with guardrails, and tuned for cost and latency. That's exactly what's missing when an LLM project ends at a demo that looked clever in the room.
- We ship LLM features in production every day, so we design for evals, guardrails and cost, not for a demo that looks clever once.
- Honest by default: not every problem needs an LLM. When deterministic code is cheaper and safer, we'll tell you instead of selling you a model.
- You leave autonomous: the prompts, evals and guardrails are documented in your repo, so your team runs and extends it without us.
- Model-neutral. We pick Claude, GPT, Gemini or open weights on fit and cost, not on a partner tier we're paid to push.
The 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. Here's what a real LLM build covers.
- 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
Model selection
We pick the right model per task across Claude, GPT, Gemini and open weights, and design for cost and latency, so you're not paying frontier prices for work a smaller or cheaper model does just as well.
- 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 model upgrade can't silently regress your feature.
- Setup
Fine-tuning & context engineering
When prompting and RAG hit a ceiling, we use fine-tuning or context engineering for the cases that need it, and we tell you honestly when a bigger model won't fix the problem.
- Setup
Deployment & observability
We ship the feature behind an API with logging, tracing and cost dashboards, so you can see what the model does in production, catch drift, and keep the 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, what to build first, 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
- The RAG, agents or evals worth building first
- The right model for the job and the cost it implies
- A frank take on what it won't fix
How we run an LLM build.
Five steps, in order. We don't ship a feature before the evals exist, we don't let an agent loose without guardrails, 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 · Architecture & data
Design the RAG, the agents and the model choice
We design the pipeline before writing it: what gets retrieved, how it's chunked and embedded, which vector DB, where agents and tool calling fit, and which model per task across Claude, GPT, Gemini and open weights. 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 · 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 evals from day one so quality is measured, not guessed. Guardrails handle hallucination control and unsafe output, observability shows what the model does in production, 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 model works where your team and your users already are, not in a separate demo, and you can 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 prompts, the evals, the guardrails and the model choices, and train your team to run, debug and extend the feature. If you want to go deeper, our AI training covers RAG, agents and the SDK end to end. If you want us on call for what scales next, we talk about that separately, but you leave able to own it.
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. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The prompts, evals and guardrails live in your repo, owned by your team
- Quality measured with evals 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 integrates large language models into your product and operations so they work reliably, instead of leaving you with a demo that impressed once. We design and build RAG pipelines, AI agents with function and tool calling, embeddings and vector DB setup on your data, evals to measure quality, and guardrails for hallucination control. We pick the right model across Claude, GPT, Gemini and open weights, optimize cost and latency, and ship it behind an API your team owns. The point is a dependable feature in production, not a prototype nobody trusts.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.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. It does the repetitive 80% without taking your team out of the decision.How do you stop the model from hallucinating?
You can't eliminate it, but you can control it, and that's a core part of the job. We ground answers in your data with RAG so the model works from real sources, add guardrails that catch unsafe or off-topic output, and build evals that measure how often it gets things wrong on your real cases, before and after every change. Observability in production shows drift early. We're honest that no setup is perfect, so we keep a human in the loop wherever a wrong answer is expensive.Which model do you use: Claude, GPT, Gemini or open weights?
Whichever fits the task and the budget. We're model-neutral and have no partner tier to push. For some work a frontier model like Claude or GPT is worth it; for high-volume or cost-sensitive cases a smaller or open-weights model self-hosted is the better call, and Gemini fits others. We pick per task, design for cost and latency, and build evals so you can compare models on your real data instead of trusting a benchmark.Do you train our team or just build it?
Both, and the handover is where most LLM projects quietly fail. A feature nobody on your side can maintain is a liability. We document the prompts, the evals, the guardrails and the model choices in your repo, and train your team to run, debug and extend it. If you want to go deeper, we run AI training that covers RAG, agents and the SDK end to end, so your team can build the next feature without us.
Stop shipping demos. Ship something reliable.
A 60-minute audit, your use cases mapped, a build plan with the evals and 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.