--- title: "LLM Consulting Company | Model, Architecture, and Cost Decisions" description: "LLM consulting on the decisions that are hard to undo: which model, self-host or API, RAG or fine-tuning, and how to keep providers swappable as the market moves." image: "https://foundrysoft.co/api/og?type=page&title=LLM+Consulting+Company+%7C+Model%2C+Architecture%2C+and+Cost+Decisions&st=LLM+consulting+on+the+decisions+that+are+hard+to+undo%3A+which+model%2C+self-host+or+API%2C+RAG+or+fine-tuning%2C+and+how+to+keep+providers+swappab%E2%80%A6" url: "https://foundrysoft.co/services/llm-consulting-company" --- LLM consulting ยท Built in India for US companies # LLM consulting for decisions that are expensive to reverse LLM consulting is most useful on the decisions that are expensive to reverse: which model family to build around, whether to self-host, how to keep providers swappable, and what your architecture assumes that may not be true in a year. Book a 30-min scoping call [See our work](https://foundrysoft.co/work) No sales script. You talk to the engineers who'd build it. 9+ hrs Timezone overlap Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies. 100% You own the IP Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell. Senior No juniors hidden on the bill You work directly with the engineers building your system. No account managers sitting between you and the people writing code. Weeks To first deployment We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month. ## What we build Concrete systems we ship, tuned to your data and your stack. ### Model selection Chosen against your evaluation set rather than a leaderboard, because benchmark scores tell you about benchmarks. ### Self-host or API An honest comparison including the operations time, which is the line that usually flips the answer back to the API. ### Portability review What in your architecture would break if you changed provider, and which of those are worth fixing now. ### Cost architecture Where the spend actually goes, and which of the usual four causes apply to you. ## How we work 01 ### Scope & evals We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed. 02 ### Build in the open Weekly demos against real data. You see progress every week and can change direction before it gets expensive. 03 ### Ship & instrument We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic. 04 ### Hand off or stay Take the keys with full docs, or keep us on for iteration. Either way you're never locked in. ## Questions, answered ### How do we choose between model providers? + Build an evaluation set from your real cases first, then run candidates against it in shadow mode. Without that, you are choosing on marketing, and every provider's marketing is accurate about a different thing. ### Is vendor lock-in a real risk? + Less than people think at the model layer and more than people think at the data layer. Swapping models is a week if your tool definitions and evaluation sets are portable. If they live in a vendor's format, it is a quarter. ### Should we build our own model? + Almost certainly not. Fine-tuning an open weight model for a narrow task, sometimes. Training from scratch, effectively never for a business application. ### How often should we revisit these decisions? + Model choice every six months against your evals, architecture yearly. More often than that and you are churning; less and you are paying for last year's prices. ## Let's scope your build. Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take. 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