Enterprise AI Development
An AI Development Company That Starts With The Problem, Not The Model
Most AI projects go wrong at the choosing, not the coding. A chatbot where a workflow was needed. A trained model where one API call would have done. We start by naming the job, the data behind it and the test that proves it worked, then build only what that answer requires.
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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Avg. Engineer Exp.
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Where AI Goes Wrong
Use Case First
Not The Hype
Senior Engineers
Vetted Only
Timezone Overlap
Live Hours
Data Handling
Scoped First
Delaware LLC
US Entity
Model Choice
Tested First
Evaluation Suite
Every Change
Human Review Points
Designed In
Running Costs
Estimated
IP Assignment
Signed
Delivery Overlap
Fixed
Hours
Models & Prompts
You
Trusted By Startups
What Our AI Development Company Actually Does
An AI development company turns a business problem into a system that runs every day without a person watching it. That means picking the approach before the tool: retrieval over a document set, a trained model on your own history, a scored decision inside an existing workflow, or nothing at all where the data will not carry it. It means writing an evaluation before writing the feature, and it means knowing what the thing costs to run at volume rather than in a demo.
Stallyons is registered in Delaware as a US company, and our engineers work 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. Teams come to us with a business problem rather than a model preference, which is the order that makes AI worth buying.
What An AI Engagement Covers
A use case chosen on evidence: the decision or task being automated, the volume behind it, what a wrong answer costs, and whether the data you already hold can support it before anybody writes a line of code.
The approach picked to fit the job rather than the trend: retrieval, a hosted model, a fine-tune, a classical model or plain software. Several of those are cheaper and steadier, and we will say so.
An evaluation set written before the build: real examples, expected answers, and a scoring method you can run yourself. Without it nobody can tell an improvement from a change of mood.
Guardrails treated as engineering: what the system may act on alone, where a person signs off, what happens on a refusal or a timeout, and how a bad answer gets reported and fixed.
Quality you can inspect: code review on every merge, evaluations rerun on every change, cost and latency tracked per feature, and work on your own board where you can read it.
One accountable vendor: one contract, one invoice and one entity for legal and finance to run diligence on, instead of a spread of contractors across four jurisdictions.
Why Buyers Screen An AI Software Development Company Hard
- Ask what happens if the data is not good enough. A vendor who has never walked away from a use case has never really looked at one before quoting for it.
- Ask to see an evaluation set. If quality is described in adjectives rather than in examples with expected answers, nobody can prove the system got better.
- Ask what it costs to run at your volume, not to build. Tokens, hosting, retries and human review are the bill that arrives every month afterwards.
- Ask where a person stays in the loop. Systems that act alone on money, health, safety or legal text need a named review step, and that step belongs in the design.
- Check the exit before you need it. Your prompts, evaluation sets, fine-tuned weights, cloud accounts and repositories all sit in your own name from day one.
- Ask how they will switch models. Providers change pricing and deprecate versions, so the integration should sit behind your own interface rather than one vendor's SDK.
How An AI Engagement Starts With Us
Every project starts with a free 45-minute scoping session. No slide deck, no sales script. You bring the process you want changed and what it costs you today; you leave with a shortlist of approaches and a build plan.
We are selective about new projects and cap how many we run at once, because the scoping is the product. If your problem is better solved by ordinary software, we will tell you that first.
Why Clients Choose Us
Full
Written IP Transfer
USA
Contract Entity
Yours
Data & Model IP
Named
Delivery Lead
Ready to find out which AI is worth building?
What Our AI Teams Build
The AI Development Work We Deliver Most
Every problem is different and the underlying builds repeat: read something, find something, predict something, decide something, or wire a model into work that already happens. These are the ones we run most often.
Conversational Interfaces
Retrieval, memory and tool calling
End-to-End
Document Systems
Extraction, review, summaries
Reads For You
Search & Ranking
Embeddings, vector stores, rerank
Relevant
Forecasting & Scoring
Demand, risk, churn, propensity
Decisions Backed
Vision Work
Detection, OCR, quality checks
Sees Detail
Speech And Audio Work
Transcription, voice, diarisation
Listening
Model Wiring & APIs
Providers, routing, fallbacks
Linked
Evaluation Harness
Golden sets, regression, human review
Measured
Custom Model Builds
Fine-tuning, training, hosting
Yours Alone
Monitoring & Support
Drift watch, cost and prompt tuning
Kept Honest
Not sure which of these you need? That is the first call.
Common Challenges
Why Do AI Development Projects Stall?
Six patterns behind almost every AI project that never leaves the pilot stage. None of them are about the model.
Solution First
01
The tool is chosen before the problem. A chatbot gets commissioned because chatbots are current, and six months later nobody can name the task it removed or the number it moved. The build was never the issue.
The Data Is Not Ready
02
The use case assumes clean, labelled, accessible history. What exists is three systems that disagree, a decade of free text and an export nobody has opened since 2019.
No Way To Measure
03
Quality is judged by whoever demos it that week. With no evaluation set and no scoring method, every change is an argument and no release can be defended.
Demo Never Ships
04
The prototype impresses in a meeting and then meets real inputs, real permissions and real load. Nobody scoped logging, retries, access control or what happens when a provider is down.
Costs Discovered Later
05
Nobody modelled the running bill. Long prompts, retries, embeddings recomputed on every change and a model called on every page view add up fast, and the pilot budget covered the build.
No Human In The Loop
06
The system acts alone on decisions carrying money, safety or legal weight. The first bad answer becomes an incident, because no review step was ever designed.
Recognise a few of these? Let us look properly.
Our AI Service Lines
The 6 AI Development Company Services
Six ways to buy AI work from one accountable vendor. Run one, or run several in parallel under a single contract.
AI Consulting & Roadmapping
01
A ranked shortlist of use cases with the data each one needs, the approach it calls for and what it would cost to run, so the roadmap argument gets settled on evidence.
Feasibility Studies
02
A short, fixed piece of work on one use case: inspect the real data, build a thin prototype, score it against a written evaluation set, and report whether it is worth funding.
Custom AI Model Builds
03
Custom AI model development services where a hosted model will not do: training or fine-tuning on your own data, hosted where your compliance rules require.
Evaluation & Guardrails
04
The scoring layer around a system somebody else built: golden sets, regression runs, refusal and escalation rules, and a reporting route for bad answers.
Data & Platform Work
05
The plumbing underneath, built with our own API development practice: pipelines, permissions, vector storage and the services a model reads from.
Production Run And Support
06
Keeping a live system honest: quality watched against the evaluation set, spend tracked per feature, prompts and model versions updated as providers change underneath you.
Not sure which piece you need first? Let us scope it together.
Why Choose Us
What Makes Our AI Development Company Different
The details that decide whether an AI system survives its first month of real traffic.
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.
Use Case Before Model
02
We settle what the system is for, and how you will know it worked, before anyone argues about which provider or framework to use.
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.
Measured, Not Claimed
04
Quality is reported against your own evaluation set, on your own examples, so improvement is something you can rerun rather than take on trust.
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 what a serious AI build looks like?
Our Process
From First Call To Live AI Development In Six Steps
A build process that settles the use case, the data and the scoring before any interface.
Discovery
Name the use case, the data and the payoff
Scoping
Agree the evaluation set, scope and cost
Design
Model choice, prompts, retrieval and fallbacks
Contracting
NDA, IP assignment, access and onboarding
Deliver
Built, reviewed and scored each week
Release & Tune
Ship behind a flag, then read the numbers
Want to see how this maps to your roadmap?
Technology Stack
What Our AI Development Teams Work With
The models, data layers and tooling we build AI systems on, and the parts that keep them running.
Models & APIs
OpenAI GPT Models
Claude Models
Google Gemini
Open Weights
Embeddings
Data & Retrieval Layer
Postgres DB
Vector Indexes
Doc Pipelines
Chunking
Feature Storage
Model Training
Python Toolchain
TensorFlow Job
PyTorch & Fine-Tunes
Experiments
Managed Training Jobs
Serving & Runtime
Container Builds
Cloud Hosts
Autoscaling
Streaming Responses
Caching & Batching
Evaluate & Watch
Eval Harness Runs
Safety Rules
Cost Telemetry
Pipelines & Releases
Drift Monitoring
Who We Build This For
What An AI Development Company Builds, By Sector
Eight kinds of organisation with different data and one shared need: a decision made faster and more consistently.
Retail & Commerce
Ranking, pricing, demand plans
Fintech & Financial Ops
Risk models, KYC, fraud checks
Health & Life Science
Triage, coding, records search
EdTech & Training
Tutoring, grading, content plans
Logistics & Supply Chain
Routing, ETAs, exception triage
Support & Service Desks
Deflection, routing, summaries
Manufacturing & IoT
Defect checks, telemetry, upkeep
Legal & Professional Firms
Clause review, drafting, search
Working in another sector? See all industries we serve.
How We Compare
Your AI Development Delivery Options, Compared
An honest look at your four delivery options.
| Capability | AI Feature In A SaaS Tool | In-House Data Team | Freelance ML Engineer | Stallyons Technologies |
|---|---|---|---|---|
| Fit to your actual use case | ✕ Whatever ships | Strong, if staffed | Depends who you hire | Chosen before the build |
| Work on your own data | ✕ Out of scope | Their day job | Often skipped | Inspected first |
| Evaluation before launch | ✕ Vendor's word | Varies by team | ✕ Rarely written | Golden set you can rerun |
| Model and vendor choice | ✕ Locked in | Whatever they know | Personal preference | Swappable behind your API |
| Running cost visibility | Seat pricing | Cloud bill only | ✕ Not modelled | Costed per feature |
| Human review and escalation | Generic settings | Added later | ✕ Not designed | Built into the flow |
| Ownership of models and prompts | ✕ Vendor's | Yours | Varies by contract | Yours from day one |
See the difference for yourself
Complete Engagement
Everything Included In Your AI Development Project
From Scoping to Contracting to Delivery, One Vendor
Here is everything included when you build your AI system with us:
One AI Development Price: No Hidden Fees, No Surprises.
Every AI engagement includes all eight components above. One contract, one senior team, one predictable cost, and no vendor sprawl.
🔒 No obligation. We'll deliver a detailed proposal within 48 hours.
Plus, Get These Free Bonuses
Free AI Use-Case Read
A written read on where AI would pay back in your operation, which use cases your data can support today, and which are not ready yet, in priority order.
Included Free
Build Plan And Estimate
A phased build plan with scope, milestones, the integrations it needs and a transparent, itemised estimate for the engagement.
Included Free
Free Vendor Checklist
The questions we would ask any AI development company about data, evaluation, guardrails and running cost, so you can test us with them too.
Included Free
Risk-Free Partnership
Our AI Development 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 AI build 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
"Stallyons took our Figma design and built it into a live web application, a cognitive game with level-based match play, messaging, a tutorial, and a directory that ranks users nationally. What impressed me most was their grasp of the code behind that logic, and the quality of the experience. Delivered on time with steady updates."
Jerry L.
Founder
PicCiti LLC
"We brought Stallyons in to absorb an overflow of work, and they delivered ten iOS and Android apps, from reporting to geo-location for logistics, plus several backend systems, owning design, development, and app-store submission. Everything stood out: code quality, speed, and reliability. Perfect code, on time, adopted company-wide."
William B.
Director
Amplo Solutions
FAQ
Frequently Asked AI Development Questions
An AI development company turns a business problem into a system that runs unattended. In practice that means choosing the approach before the tool, checking whether your data can carry the use case, writing an evaluation set that says what a good answer looks like, building the feature behind your own interface so the model can be swapped, and putting a person in the loop wherever a wrong answer would cost real money.
Start from the task, not the technology. Answering questions over your own documents is a retrieval problem. Doing a multi-step job end to end is an agent build. Removing manual handoffs between systems that already work is automation. Predicting a number or a class from your own history is machine learning. Reading images or scanned paper is a vision problem. We name the task first, then take the shortest route to it, which is often not the one the market is talking about.
There are two bills and they behave differently. Building is scoped work, priced from the use case, the state of the data and how many systems it touches. Running is per use: model calls, retries, embeddings, hosting and any human review step. Cost per request usually matters more than build price at volume, so we model both during scoping and itemise each phase.
Ask what they would refuse to build and why. Ask to see an evaluation set from real work, because quality described in adjectives cannot be rerun. Ask what the system costs per request at your volume. Ask where a person reviews the output. Then ask who owns the prompts, weights and accounts. The answers separate engineering from enthusiasm quickly.
You do, from the first day. Prompts, evaluation sets, fine-tuned weights, pipelines and repositories are yours, and the cloud and model provider accounts are registered in your name. NDA and IP assignment are signed before anyone gets access, and everything written on the engagement is assigned outright.
Not for every use case. Work built on a hosted model and your own documents can start with what you already have, because retrieval reads the documents rather than learning from them. Training or fine-tuning a model is different and needs volume, labels and consistency. The feasibility study exists to tell you which of those you are in.
With an evaluation set written before the build: real inputs from your own operation, the answer each one should produce, and a scoring method you can run without us. It reruns on every prompt, model or retrieval change, so a release is defended with a number rather than an impression. Failures are logged and fed back in.
Yes, and it is a common shape. Your team keeps the product and the domain knowledge while we take the AI layer, the evaluation harness and the data plumbing, working on your board and in your repositories. Where you already have a model in production we can take the guardrails and monitoring work only, without touching the rest.
Still have questions? Let's talk.
Schedule an appointment with us today!
Ready To Put AI Where It Will Actually Pay Back?
Get a free consultation. We will walk your operation, name where AI would earn its cost, and send a written proposal.
