AI Services
RAG pipelines, agentic workflows, and LLM integrations scoped to what actually works.
Most “AI features” die in the gap between a demo and production. Wiring up a call to an LLM API takes an afternoon. Retrieval that finds the right paragraph in your actual documentation, a prompt that survives someone phrasing a question sideways, a sane fallback for when the model is confidently wrong, and a cost curve that doesn’t triple the month your usage does — that’s the part most teams skip, and it’s the part we build.
What we build
Most of our AI work falls into three buckets, usually combined on the same project. The first is retrieval-augmented generation: we chunk and embed your documentation, product catalog, or support history into pgvector if you’re already running Postgres, or Pinecone if you’d rather not manage your own index, then build the retrieval layer that grounds answers in your actual content instead of whatever the model picked up during training. The second is integration and orchestration — connecting OpenAI, Anthropic’s Claude, or a self-hosted open-weight model into your product, including agentic workflows where the model calls tools, queries your API, or hands a step off to an n8n automation as part of a longer task. The third is the review layer: a queue that routes anything below a confidence threshold, above a certain dollar value, or flagged as high-risk to a human before it goes out, because almost every production system we’ve shipped needed a checkpoint somewhere — full autonomy is rarely the right first version.
How we scope and ship it
We start with one question: which parts of this task does a model actually help with, and which parts are better served by a plain database lookup or an if/else rule? That conversation cuts scope before we write any code, which is usually the biggest cost saver in the whole engagement. From there we build a narrow prototype against a real slice of your data — a working pipeline, not a chat widget with nothing behind it — and score it against a test set of real inputs before calling it done. We default to prompt engineering plus RAG first, because it’s fast to iterate and doesn’t require a training run or a labeled dataset. Fine-tuning only earns its cost when the task is narrow and well-defined, you already have a solid set of labeled examples, and prompting has genuinely hit a ceiling on accuracy or consistency — for most projects, that point never arrives. Once something’s live, we route high-volume simple tasks to cheaper, faster models and save the expensive ones for steps that actually need the reasoning, because unmanaged token cost is the single most common reason we see an AI feature get pulled a few months after launch.
When this isn’t the right service
We’ll also tell you where an LLM is the wrong tool, even when “add AI” was the original ask. If a workflow needs a guaranteed, auditable outcome — calculating a refund, approving a loan, flagging a medical claim — a deterministic rule or a lookup table beats a model on cost, speed, and consistency every time. LLMs are strong on unstructured input: summarizing a messy support ticket, classifying intent, drafting a first-pass reply a human edits before it sends. They’re not reliable enough yet to be the last word on anything with real financial, legal, or safety consequences without a person checking the output, and we’ll scope a human-in-the-loop step into that kind of project whether or not it was asked for. If what you actually want is full autonomy on a high-stakes decision with no review step, we’re not the right studio for that particular piece of work.
What this is meant to achieve
The measures we hold the work to, agreed with you before the build starts.
Support and sales teams get a grounded first draft instead of a blank page, so replies go out faster without losing the human check before they send
Teams stop digging through internal docs by hand because the answer comes back cited to the source
Token spend stays predictable because routing and caching are designed in from the start, not patched in after a surprise invoice
Switching or adding a model provider later is a configuration change, not a rebuild, because the integration layer isn't locked to one vendor
You get a system you can explain in a security review or a board meeting, not a black box nobody on the team can account for
Questions people ask before signing
Straight answers on pricing, code ownership, timelines and what happens after launch.
For almost every project, prompt engineering plus retrieval-augmented generation gets you further, faster, and it’s what we build first. Fine-tuning is worth the cost when you have a narrow, well-defined task, a real volume of labeled examples, and you’ve already hit a ceiling with prompting alone — think consistent formatting on a repetitive classification task, not general-purpose chat. We’ve had clients ask for fine-tuning as a first move and talked them out of it because the underlying task didn’t need it yet.
It depends on the task, and we build the integration layer so switching providers later is a config change, not a rewrite. We reach for Claude models when the task involves long documents, careful instruction-following, or tool use in an agentic workflow; GPT-4o and its smaller variants are a strong default for general-purpose chat and classification at lower cost. For clients with strict data-residency requirements, we’ve also deployed self-hosted open-weight models on a private VPS — slower to set up and usually a step behind the frontier labs on raw quality, but the data never leaves your infrastructure.
We ground answers in your actual content through retrieval instead of letting the model answer from memory, require citations back to the source document where it matters, and route anything below a confidence threshold to a human instead of letting it guess. Before anything ships, we run it against a test set of real questions and score the output, not just eyeball a demo. None of that gets hallucination to zero — nothing does — but it turns an unpredictable failure mode into a known one you can catch and fix.
It can, if nobody’s watching the token spend — that’s the most common reason we see AI features get pulled months after launch. We build in model routing from day one: cheap, fast models handle high-volume simple tasks, and the expensive ones are reserved for steps that genuinely need the reasoning. Caching repeat queries and trimming context down to what the model actually needs cuts spend further. We’ll give you a realistic monthly estimate before we build, not after.
Both, if you need it, but the interface is usually the easy part. If you’ve already got a UI and just need the RAG pipeline, the agent logic, or the review queue behind it, we scope it that way and skip rebuilding what already works.
That’s the scenario we design around from the start, not a risk we hope doesn’t happen. Low-confidence or high-stakes outputs route to a human review queue before they reach a customer, and we build the model’s fallback response to be an honest ‘I don’t know, let me get someone’ instead of a confident guess. If a project can’t tolerate any wrong answers reaching a customer, we’ll say upfront whether an LLM should be in that path at all.
Ready to talk about AI Services?
Tell us what you need built. You will get a written proposal with scope, timeline and price before any work begins.