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LLM models · AI · Los Angeles

LLM consulting for US teams that want the right model for each job

Six model families, dozens of sizes, prices that move every quarter. You need a short list and a fair test.

Large language models are the engines behind AI (now also called SI, super intelligence) tools: they read text, write it, pull facts out of documents and decide which step comes next. Our team, led from Glendale, helps you pick between OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral and open models from Hugging Face, tests the shortlist on your own records, sets up where it runs, and connects it to Zoho and AWS.

  • Chosen per task, never by hype
  • Graded on your own samples
  • Runs where your data may go
Book an AI / SI call Try the model chooser
AWS AWS PartnerAlso a Zoho Advanced Partner
You’ll talk to our senior team, not a call center.300+ projects · serving US clients since 2025

01Is an LLM the right tool?

When does a large language model earn its keep?

An LLM shines on messy language: emails, call notes, contracts, product copy, support threads. It is the wrong tool when the answer must be exact every time and a formula or a database query already gives it. We sort every idea into one of these two piles before talking about vendors.

Strong uses

  • First drafts a person checksReplies to customers, quote cover letters, follow-up emails after a call.
  • Pulling fields out of documentsInvoice totals, policy numbers or lease dates read from PDFs into Zoho records.
  • Answering from your own filesAn assistant that quotes your handbook or price sheet, with the source shown.
  • Sorting and routing at volumeTagging inbound leads, tickets or reviews by topic, urgency and language.

Weak uses

  • Math that must balancePayroll, tax and ledger totals belong to Zoho Books or code, with the model at most explaining the result.
  • Final decisions about peopleHiring, lending or medical calls need a human owner and a documented reason.
  • Facts it was never givenAsk about this week’s inventory without connecting the data and it will guess, confidently.

02LLM services

What our LLM consulting covers, from shortlist to production

Picking the model is maybe a fifth of the work. The rest is getting the right data in front of it, proving it answers well, keeping costs predictable and making sure nothing private leaks out. These are the pieces a typical engagement includes.

  1. Model shortlist per use case

    For each task we name two or three candidates, the tier to start with and where each would run, with the reasoning written down so your team can challenge it.

  2. Evaluation on your own records

    A graded test set built from real tickets, emails or documents, scored blind by the people who own those answers, then rerun whenever a vendor ships something new.

  3. Retrieval over your documents

    Help articles, SOPs and price sheets split, indexed and searched so the model answers from your content and cites the page it used.

  4. Private hosting and access control

    Hosted models reached through Amazon Bedrock or your existing cloud, or open-weight models on GPU instances in your own AWS account, with keys and logs locked down.

  5. Prompts, guardrails and cost limits

    Versioned prompts, output checks, spend alerts and a fallback model, so one vendor outage or a runaway loop doesn’t stop the business or the budget.

03One task, five forks

How we picked a model for drafting support replies

A sample case: a plumbing supply distributor in Phoenix, AZ with a 14-person support team on Zoho Desk wants a first draft waiting on every new ticket. Each fork below is a real choice we make on projects like this, and the road not taken matters as much as the one we take.

  1. What exactly is the job?

    Draft a reply using the help center and the customer’s last orders. An agent reads it, edits it and presses send.

    Draft with retrieval, agent sendsAuto-reply with no human

  2. What data will the model see?

    Names, emails, shipping addresses, order numbers. Nothing medical, but plenty that a customer would not want floating around.

    Keep calls inside their AWS accountStaff pasting tickets into a chat app

  3. How much volume, how fast?

    About 350 tickets on a busy day. A draft that appears within half a minute is plenty, since agents open tickets in order.

    Mid-size tier, largest tier on escalationsLargest model on every ticket

  4. Where should it run?

    The company already runs its order system on AWS, so Amazon Bedrock puts usage on the same invoice and under the same access rules.

    Amazon Bedrock, two candidate modelsRenting GPUs for one use case

  5. Which candidate actually wins?

    Sixty past tickets, answered by both candidates, graded blind by two team leads for accuracy, tone and policy. The scores, not a leaderboard, decide.

    Blind grading on their ticketsPicking whatever trended this month

  6. Live, with a spare ready

    The winner goes live behind Zoho Desk, the runner-up stays configured as a fallback, and the sixty-ticket test set is kept so the next model release can be checked in an afternoon.

Sample company, volumes and test size. No vendor rankings are implied; the right winner differs from one business to the next.

04Try it · model chooser

Which LLM should you test first? Three questions.

Tell us the task, how sensitive the data is and what matters most on cost. You get a hosting route, a size tier and a shortlist to test. It reflects how we narrow the field, and it makes no claim about which model scores highest.

1The task
2Data sensitivity
3Cost priority

Hosting route Sample advice

Start with

    Sample guidance only. On a real project the shortlist is checked against your contracts, your cloud and the vendors’ current terms, then proven on your own data.

    05LLMs + Zoho

    Where does the model plug into Zoho?

    There are four doors, and most of our clients use two of them. Which ones depend on whether you want the AI / SI inside Zoho screens, acting on Zoho from an assistant, or working quietly in the background.

    Inside Zoho, through Zia

    Zia can run on Zoho’s own model or on a provider key you bring. We pick the provider, set it up and decide which apps use it. See Zoho Zia.

    From an assistant, through MCP

    Claude or ChatGPT reads and updates Zoho records with the permissions you allow, one approved tool at a time. See Zoho MCP.

    In the background, on an event

    A new ticket or lead fires Zoho Flow or a webhook, the model drafts, tags or summarizes, and the result lands back on the record as a note or field.

    Custom logic in your own code

    Retrieval, retries and logging live in a Zoho Catalyst function or an AWS Lambda, so the prompt and the keys never sit in a browser.

    06LLM pricing

    How are LLMs priced for business use?

    Two models of billing cover almost everything: hosted models charge per token, and open-weight models you host charge you for the hardware by the hour. Per-token list prices change too often to print here responsibly, so we pull current rates for your shortlist during scoping and model the monthly bill from your real volumes.

    Hosted APIs and cloud marketplaces

    • Input and output tokensBilled per million tokens, with output usually priced higher than input. A token is roughly three quarters of an English word.
    • Model size tierWithin one vendor the smallest tier can cost a small fraction of the largest, which is why routing easy work to small models matters.
    • Caching and batchReused prompt text and overnight batch jobs are discounted by most vendors.
    • Through your cloudAmazon Bedrock bills on your AWS invoice, on demand or with reserved throughput.

    Open weights you run yourself

    • LicenseLlama, Mistral’s open models and many Hugging Face models can be downloaded at no charge, each under its own license terms.
    • GPU hoursThe real cost is the instance: hourly whether busy or idle, so steady high volume is where self-hosting pays back.
    • People timeUpdates, scaling and monitoring are yours, or ours under a support plan.

    Our side never rides on token counts. The shortlist, test set, hosting and Zoho wiring are written into a BRD you sign, with one fixed figure for the build. The first call costs you nothing; usage is billed by the model vendor or AWS directly to you.

    07LLM questions

    LLM questions US businesses ask us

    Which vendor, what it costs, whether your data is used for training, and when open models make sense. Bring the task you have in mind to the first call and we’ll talk through a shortlist.

    Book an AI / SI callOpen the model chooser →

    Which LLM is best for a business?

    There is no single best model. The right pick depends on the task, how private the data is, how many calls you make and which cloud you already use. We shortlist two or three candidates for each task and grade them on your own examples, because public leaderboards rarely match the work a business actually does.

    ChatGPT vs Claude vs Gemini: which should we use?

    All three are capable hosted model families with small, mid and large tiers. Practical differences often decide it: Claude is offered on Amazon Bedrock, Gemini lives on Google Cloud, and OpenAI models are also sold through Microsoft Azure. We test the ones that fit your cloud and data rules on your own samples before you commit.

    Do LLM providers train on our business data?

    The major vendors state that data sent through their paid business APIs is not used to train their models by default, while consumer chat apps can have different settings. Terms change, so we check the current terms for your exact plan and region during scoping, and route sensitive work through your own cloud account.

    Can we run an LLM privately inside our own AWS account?

    Yes. Models such as Claude, Llama and Mistral can be called through Amazon Bedrock, which keeps traffic and billing in your AWS account. For full control, open-weight models can run on GPU instances you own. We set up the access rules, logging and network paths either way.

    Should we use an open-source model like Llama or Mistral?

    Open-weight models make sense when you need the model on your own hardware, want to fine-tune it deeply, or run steady high volume where GPU hours beat per-token fees. For low or bursty volume, a hosted API is usually cheaper and less work. Each open model has its own license, which we review with you.

    How much does it cost to use an LLM?

    Hosted models charge per million input and output tokens, with large differences between small and large tiers, plus discounts for cached prompts and batch jobs. Self-hosted models cost GPU hours instead. We estimate your monthly usage from real volumes during scoping; our own build is a fixed price in a signed BRD.

    Do we need to fine-tune a model?

    Usually not at first. Most business tasks work well with a clear prompt plus retrieval from your own documents, which is faster to change when your policies change. Fine-tuning helps for narrow, repetitive formats or very high volume on a small model, and we try it only after the simpler route has been measured.

    Can you connect an LLM to Zoho CRM or Zoho Desk?

    Yes. Common routes are Zia with your own provider key, Zoho MCP so Claude or ChatGPT can act on records, and Zoho Flow, Zoho Catalyst or AWS Lambda calling a model when a record changes. The output lands back in Zoho as a draft, a tag or a summary for a person to review.

    What is SI?

    SI stands for super intelligence. In September 2026 a US executive order asked federal agencies to use “super intelligence” or “SI” in place of “artificial intelligence” in official communications. Most businesses still say AI, so we use both terms.

    08How an LLM project starts

    Before any model is chosen, the task gets a test

    Four steps take you from “we should use AI / SI for this” to a signed plan. One clear task can often be shaped on the first call; several use cases with sensitive data may need a short paid discovery first, priced before it starts.

    Model work happens on video calls and in your cloud console wherever you are in the US, Canada or Latin America; teams near Los Angeles can have us in the room for the grading sessions.

    1. Discovery callThe task, the data it touches, your cloud and what Zoho already holds
    2. Sample setTwenty to a hundred real examples with the answers your team would give
    3. Shortlist and routeTwo or three candidate models and where each would run
    4. BRD sign-off Our promiseTest plan, hosting, Zoho links and limits in writing, at a fixed price

    Once signed, the build follows our standard new-project track. See how we work →

    09Let’s talk

    Tell us what you’re building. We’ll map the fix.

    Book a free consultation with Elite Tech Global. We review your Zoho, AWS, Shopify or WordPress setup, show where AI / SI agents can save time, and agree the next step. If we work together, every requirement and a fixed price go into a signed BRD before the build starts.

    Real people, one team. Led by co-founders Ikramul Karim and Khris Kakosimidi, and a 40+ member team across Zoho, AWS and AI.

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