Our LLM Integration Services
LLM Integration Services That Make The Model Layer Behave
Most LLM work reaches us answering well in testing and unpredictably in front of users. We rebuild the model layer underneath: retrieval, prompts, guardrails and evaluation you can actually read. Stallyons is a Delaware-registered US company, and every release goes out through the same review.
Triple Protection Guarantee
- US-Registered Entity
- Signed IP Assignment
- Senior Engineers Only
Triple Protection Guarantee:
- US-Registered Entity
- Signed IP Assignment
- Senior Engineers Only
Years In Business
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Engineers On Staff
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The Model Layer
Model Selection
On Evidence
Retrieval & RAG
Grounded Only
Embedding Design
Chunked Up
Prompt Layer
Kept Versioned
Guardrails
Enforced
Data Security
Access Rules
Eval Harnesses
Every Change
Communication Rhythm
Daily Sync
Token Budget
Modelled
IP Assignment
Signed
Delivery Overlap
Fixed
Hours
Code & IP Owner
You
Trusted By Startups
What LLM Integration Services Actually Require
LLM integration is the engineering underneath an AI feature: which model, given what context, checked how. A language model is a component with no memory of your business and no obligation to be right. Making it behave means deciding what goes into the context window and what stays out, retrieving the correct passage rather than a plausible one, constraining the output shape, and measuring answers against a set you keep. Prompting is the visible tenth of it.
Stallyons is registered in Delaware as a US company, and our engineers work inside a time-zone window agreed before the project starts, with four-plus hours of daily overlap. You sign a US contract, run diligence on a US entity and pay one US invoice. Behind the work sits twelve-plus years of delivery across six continents and around thirty-five engineers, averaging four-plus years of production experience. Founders, CTOs and data leaders across North America, the UK, Europe, the Middle East and Asia-Pacific bring us the model layer.
What LLM Integration Covers
Model selection on evidence, not preference: candidates are compared on your own tasks and your own data, with quality, latency and cost per call recorded, so the choice can be explained to somebody who has to sign it off.
Retrieval that returns the right passage: how documents are split, what gets embedded, how results are ranked and re-ranked, and what happens when nothing relevant exists rather than answering anyway.
The context window treated as a budget: only what the task needs is assembled into it, because stuffing every possible document in raises cost and latency while making answers measurably worse.
Guardrails on the way in and out: input validation, output shape enforced by schema, refusal behaviour defined, and citations back to source so a claim can always be traced.
An evaluation set you own: real questions with agreed correct answers, run automatically on every prompt, model or retrieval change, so nobody ships an improvement on instinct.
A launch that does not end the engagement: monitoring answer quality, retuning as your content shifts, and a written handover, so the model layer keeps holding up.
Why Companies Choose Our LLM Integration Engineering
- The evaluation set is built before the prompts are. You approve a set of real questions and correct answers, and every later change is measured against it rather than argued.
- Model choice is justified in writing. You see how each candidate scored on your own tasks for quality, latency and cost, and why the recommended one won.
- Work lands on your board in your repository. Progress is something you read whenever you want, not something summarised at you every Friday.
- Every merge is reviewed against an agreed definition of done, and the evaluation suite runs on each pull request, so a prompt change cannot quietly degrade answers.
- Token and latency cost is modelled before launch. You see expected spend per query and per user, with the context budget sized deliberately rather than by accident.
- Handover is written while the work happens: prompts, evaluation sets and runbooks, so your own team, or whoever comes after us, reads instead of guessing.
How An LLM Integration Project Starts
Every project starts with a free 45-minute design review. No slide deck, no sales script. You bring the task, the content it must answer from and any current prompts; you leave with an approach, scope and timeline.
We are selective about new projects and cap how many we run at once, because the design review is the product. If your content is not ready for retrieval yet, we will say so on that first call.
Why Clients Choose Us
Full
Written IP Transfer
USA
Contract Entity
Yours
Prompts & Evals
Named
Delivery Lead
Ready to make your model layer behave predictably?
What We Build With LLMs
LLM Integration Projects We Deliver
LLM work holds up when retrieval, prompts and evaluation are designed together instead of tuned by feel. These are the projects we deliver most often, each with an agreed scope, an overlap window and one review standard.
RAG & Retrieval Setup
Retrieval over your own content
Grounded
Vector Databases
Embeddings, indexes, re-ranking
Tuned Recall
Model Evaluation
Candidates scored on your own tasks
Measured
Prompt & Context Design
Templates, context, versioning
Live In Production
Eval Suites
Regression runs on every change
Automated
Guardrails & Schema
Structured output, refusals
Enforced
Fine-Tuning Decisions
When it beats prompting, and when
Judged
Self-Hosted Models
Open weights where residency demands it
In House
Token & Latency Work
Caching, routing, context sizing
Cost Modelled
Support & Maintenance
Monitoring, fixes, releases, cover
Kept Running
Not sure why answers vary? Let's look at the model layer.
Common Challenges
Why Do LLM Features Misbehave?
Six patterns behind almost every LLM feature that demos beautifully and then answers badly in front of real users.
Prompts By Feel
01
Prompts get edited until a few test questions look right, with nothing recording whether the change helped or hurt everything else. Each improvement is a coin flip nobody is measuring.
Retrieval Misses
02
Documents are split at a fixed length that cuts through the middle of the answer. Retrieval returns something adjacent, and the model confidently fills the gap.
Context Overstuffed
03
Everything remotely related is pushed into the window on the theory that more helps. Cost and latency climb while the relevant passage gets lost among the noise.
No Eval Harness
04
Quality is whatever the last person to try it thought. With no scored set of real questions, nobody can say whether this week's version is better than last month's.
Locked To One Provider
05
Provider calls are scattered through the codebase with prompts hardcoded beside them. A better or cheaper model arrives and switching becomes a refactor nobody has budget for.
Hallucination Unmeasured
06
Wrong answers are treated as anecdotes rather than counted. Without citations or a refusal path, nobody can tell a grounded answer from an invented one.
Recognise the pattern? Let's measure it properly.
Our LLM Engineering
Six LLM Integration Services We Offer
Six ways to buy LLM engineering from one accountable vendor. Run one, or run several in parallel under one contract.
RAG & Retrieval Pipelines
01
Retrieval over your own content, designed end to end: chunking, embeddings, indexing, ranking and citation, so answers come from your material rather than the model's memory.
Model Selection Work
02
Candidate models compared on your own tasks and data, scored for quality, latency and cost per call, ending in a written recommendation you can hand to whoever signs it off.
Prompt & Context Design
03
Prompt templates, context assembly and versioning held in the codebase and reviewed like code, so a change is traceable to whoever made it.
Evaluation & Regression
04
A scored set of real questions and correct answers, run automatically on every prompt, model or retrieval change, so quality is a number and not a feeling.
Guardrails & Grounding
05
Structured output enforced by schema, defined refusal behaviour, and citations back to source, so a claim can be traced instead of taken on trust.
Token & Latency Engineering
06
Context sizing, caching, batching and routing cheaper work to smaller models, so spend and response time are designed rather than discovered on the invoice.
Not sure which LLM problem you have? Let's scope it together.
Why Choose Us
What Makes Our LLM Integration Services Different
The details that decide whether an LLM feature still answers well in month six.
A US Legal Entity
01
Stallyons is registered in Delaware. Your contract, your invoice and your legal recourse sit with a US company, not an unknown one.
Measured, Not Argued
02
The evaluation set exists before the prompts do, so every change to the model layer is a measured result rather than an opinion.
Overlap You Set
03
You choose the hours we share with your working day, and stand-ups, reviews and escalations all happen inside that window.
No Provider Lock-In
04
Providers sit behind one interface with prompts kept out of the call sites, so moving to a better or cheaper model is a configuration change.
Reviewed Code
05
Every merge is reviewed against an agreed definition of done, on your board, where you can read it yourself.
One Contract
06
One contract covers the engagement, so procurement, legal and finance each deal with a single named counterparty.
Ready to see how we engineer the model layer?
Our Process
From Eval Set To LLM Integration In Six Steps
A delivery process that builds the measuring stick before it builds the model layer.
Discovery
Understand the task, content and correctness
Scoping
Agree scope, milestones and cost structure
Design
Retrieval, prompts and evaluation set agreed
Contracting
NDA, IP assignment, access and onboarding
Deliver
Work on your board, reviewed on merge
Tune & Monitor
Re-run evals, retune as content changes
Want to see how this maps to your roadmap?
Technology Stack
The Stack Behind Our LLM Integration Work
The model-layer toolchain we build with, from retrieval and embeddings through to evaluation runs.
Model Access
OpenAI Platform
Google Gemini
Anthropic Claude
Open Weights
Self-Host
Retrieval & Embeddings
pgvector DB
MongoDB Vectors
Chunking Rules
Ranking
Citation Tracking
Prompt & Eval
Prompt Registry
Eval Harness
Regression Suites
Schema Out
Guardrail Policies
Data & Training
Python Tooling
TensorFlow
Fine-Tunes
Token Accounting
Latency Tracing
Cloud & DevOps
AWS / GCP / Azure
Docker / K8s
Terraform / IaC
GitHub Actions / CI
Quality Dashboards
Industries We Serve
LLM Integration For Industries Where Answers Must Be Right
Engineers who already know your terminology, source documents and edge cases spend month one building.
Healthcare & HealthTech
Protocols, coding, cited answers
Retail & E-Commerce
Catalogue search and comparison
EdTech & Learning
Curriculum retrieval and marking
SaaS & Digital Products
Answers grounded in your docs
Logistics & Supply Chain
Contract, tariff and rule text
Manufacturing & IoT
Manuals, specs, fault history
Agencies & Consultancies
White-label model-layer work
We know your source material. Let's design the layer.
How We Compare
RAG vs Fine-Tuning vs Prompting Alone
An honest look at how the approaches compare.
| Capability | Prompting Alone | Fine-Tuning Only | Managed AI Platform | Stallyons Technologies |
|---|---|---|---|---|
| Answers from your content | ✕ Model memory only | Baked in, static | Black-box retrieval | Retrieved and cited |
| Updating knowledge | ✕ Not possible | ✕ Retrain each time | Vendor schedule | Re-index on change |
| Quality measurement | ✕ By feel | Training metrics | Vendor dashboard | Your own eval set |
| Tracing a wrong answer | ✕ No source | ✕ No source | Partial logs | Citation to passage |
| Switching model later | Prompt rewrite | ✕ Retrain from zero | ✕ Vendor lock-in | Behind one interface |
| Token & latency cost | Grows with context | Training spend | Per-seat pricing | Budgeted and cached |
| Data residency | Provider decides | Provider decides | ✕ Vendor cloud | Self-host where needed |
| Contracting entity | Platform terms | Platform terms | Vendor terms | US-registered LLC |
See the difference for yourself
Complete Engagement
Everything Included In An LLM Integration Project
From Scoping to Contracting to Delivery, One Vendor
Here's everything included in an LLM integration project:
One LLM Integration Project Price: No Hidden Fees, No Surprises.
Every LLM integration project includes all eight components above. One contract, one senior team, one predictable cost, no vendor sprawl.
🔒 No obligation. We'll deliver a detailed proposal within 48 hours.
Plus, Get These Free Bonuses
Free Model Layer Read
A written read on whether retrieval, fine-tuning or better prompting fits your task, and what has to be true about your content before any of it works.
Included Free
Delivery Plan & Estimate
A phased delivery plan with scope, milestones, a stack recommendation and a transparent, itemized estimate for the engagement.
Included Free
Free Eval Starter Set
The questions we would ask any LLM team about retrieval, evaluation, guardrails and token cost, so you can put us through the same test.
Included Free
Risk-Free Partnership
Our LLM Integration Promise
We stand behind every engagement with commitments that protect your investment.
01
Scope Agreed First
Scope, model, working hours and cost structure are written down and agreed before contracting, so nothing is discovered later.
02
Built to Last
Senior developers, code review, automated tests, security and accessibility audits, and clean, documented code you fully own.
03
IP And Access Protected
NDA and IP assignment are signed before access, permissions are scoped per person, and your accounts stay under your control.
Start your LLM integration with confidence, backed by our Triple Protection Guarantee.
Track Record
Engagements That Ship, Scale, and Compound
500+
Projects Delivered
29+
Service Categories
81%
Repeat Client Rate
4.9 ★
Clutch Rating
"We came to Stallyons after burning two years and four vendors on a multi-platform launch that kept slipping. They scoped it end-to-end — web app, iOS, Android, an AI summarization layer, and a Shopify integration — and shipped it in 22 weeks. One team, one budget, one quality bar. We've handed them three more engagements since."
Mark Sawyer
CEO/Founder
PlatinumLED
"Stallyons rebuilt our customer-facing portal, integrated three legacy systems, shipped an AI document analysis pipeline, and brought our compliance posture to SOC 2 — all under one engagement. The senior engineers on the team have shipped at companies five times our size. It's the best vendor decision we've made in a decade."
Mark Sawyer
CEO/Founder
PlatinumLED
FAQ
Frequently Asked LLM Integration Questions
LLM integration services build the model layer underneath an AI feature so it answers reliably. The work covers model selection and evaluation, retrieval and RAG pipelines, embeddings and vector storage, prompt and context engineering, guardrails, structured output, an evaluation harness and token and latency budgeting, then support once it is live. What you buy is a model layer whose behaviour is measured.
AI integration is about the systems around the model: where the call belongs in an existing application, permissions, rate limits, fallbacks and rollout. LLM integration is about the model itself, and whether the answer coming back is correct, grounded and affordable. One connects the plumbing, the other decides what flows through it. If your problem is that AI is not wired into your software, that is integration. If it is wired in but the answers cannot be trusted, that is this page.
Cost follows the content, the accuracy the task demands and how much evaluation is needed, so a number quoted before a design review is a guess. What moves it most is the state of your source material, whether retrieval is straightforward or the documents need restructuring, and how strict correctness has to be. We review, then price and itemise it.
Usually retrieval first. RAG suits tasks where answers must come from content that changes, because you re-index instead of retraining, and every answer can cite a source. Fine-tuning suits a fixed style, format or classification the model keeps getting wrong. They combine well, and the design review says which your task actually needs.
You do, from the first commit. The NDA and IP assignment are signed before any access is granted, with no licence-back and no shared ownership. Prompts, evaluation sets, embeddings and the repository live in your own accounts from day one, and nothing has to be handed back at the end.
By grounding and by measurement, not by asking the model to be careful. Answers are built from retrieved passages and cite them, the output shape is enforced by schema, and refusal is a defined behaviour when nothing relevant is found. An evaluation set then counts how often answers are wrong, so the rate is a tracked number.
Yes, where residency rules, sensitivity or cost at volume justify it. Open-weight models can run inside your own environment so no content leaves it, at the cost of hosting and slightly more operational work. We keep providers behind one interface, so hosted and self-hosted models stay swappable either way.
It drifts, which is why the evaluation set matters more than the launch. New and edited documents are re-indexed, the suite is re-run so any regression shows as a number, and retrieval or prompts are retuned. Most clients continue on a support cadence covering re-indexing, evaluation runs and model updates.
Still have questions? Let's talk.
Schedule an appointment with us today!
Ready To Build An LLM Layer That Behaves?
Get a free consultation. We'll review the task, recommend a model-layer approach, and send a detailed written proposal.
