Build
Custom AI Development CompanyBuilt for your problem.
Production copilots, assistants, and domain models, engineered to ship. Built for your specific problem, evaluated rigorously, and hardened for the real world.
- production
- grade, not demos
- evaluated
- continuously
- owned
- by you
- 3-8 wks
- to first ship
Model-agnostic orchestration layer.
tightly coupled to one vendor
Eval gate on every release.
Built for your problem.
Demos are easy; production is hard. Anyone can wire up a model call and record a screen capture. The real work is what happens after: eval harnesses, observability, guardrails, CI/CD, and the unglamorous engineering that keeps an AI system accurate when real users hit it with real data and real edge cases. arosplatforms is a custom AI development company that builds systems designed to survive that contact.
Our AI development services run full-stack, from custom LLM applications and agentic systems to fine-tuned domain models, with evaluation and hardening from day one, so quality is a number you can track, not a feeling.
Every engagement is fixed-scope and transparently priced, and it ends with your team owning the code and the models. No black box, no lock-in, measurable ROI targeted within 12 months.
From spec to production.
Scroll through it, the screens move with you.
Designed to ship, not to demo
A clear architecture and spec, model-agnostic and built to evolve, before we write code.
Model-agnostic orchestration layer.
tightly coupled to one vendor
Eval gate on every release.
Quality you can measure
Eval harnesses score accuracy, safety, and regressions on every change, so quality is a number.
Shipped in the open
CI/CD and observability from day one, with your team alongside the whole way.
Model-agnostic orchestration layer.
tightly coupled to one vendor
Eval gate on every release.
Everything in the engagement.
Custom LLM applications
Copilots, assistants, and tools built around your specific workflow and data.
Fine-tuning & evaluation
Fine-tune and evaluate models so they perform on your tasks, measurably.
Full-stack delivery
From data and models to UI and APIs, we deliver the whole system.
Production hardening
Eval, observability, guardrails, and the engineering that keeps it reliable.
Eval harnesses
Automated evaluation so quality is a number you can track and defend.
Owned by you
Deployed in your environment, your team owns the code and models.
A clear path from kickoff to value.
Scope & align
We align on goals, constraints, and what success looks like, then scope a focused engagement with a clear baseline.
Assess & design
We assess your starting point and design the approach, architecture, and sequencing before a line of code.
Build & deliver
We build and ship in the open, with checkpoints and your team alongside, never a black box.
Operate & hand over
We harden, document, and hand over. Your team owns it, with managed support where you want it.
Results you can measure.
Production systems
that survive real users
Quality as a number
eval gates on every release
No lock-in
your team owns all of it
Built around your starting point.
Building AI features
Product teams
Ship reliable AI features without rebuilding the infra.
Internal copilots
Operations
Automate real workflows with hardened, evaluated systems.
Specialized tasks
Domain models
Fine-tune models that beat generic ones on your work.
AI development services we offer
Our custom AI software development practice covers the full stack: LLM applications, agentic AI, RAG pipelines, fine-tuned domain models, computer vision, document intelligence, and MLOps. We do not wrap off-the-shelf APIs and call it done. Every system is purpose-built around a specific problem, a specific data environment, and a specific definition of what "working" actually means.
Custom LLM applications
Copilots, assistants, and domain-specific tools built around your workflows and proprietary data, not generic chat interfaces bolted onto a public model. We design the orchestration layer to be model-agnostic from day one, so you are not locked into a single provider when a better option ships six months later.
Agentic AI systems
Multi-step autonomous agents that execute workflows, not just answer questions. The difference matters: a question-answering system surfaces information, an agent acts on it, routing a document, triggering a procurement request, or escalating an exception. Our AI agents and automation practice builds agents that handle the full loop with defined guardrails and human-in-the-loop checkpoints where the risk warrants it.
RAG pipelines and knowledge systems
Retrieval-augmented generation over proprietary document sets, structured databases, or real-time feeds. RAG is the right architecture when you need grounded, auditable answers from your own knowledge base, not hallucinated summaries of public training data. We design the retrieval layer, chunking strategy, embedding model, vector store, and reranking as carefully as the generation layer, because retrieval quality is where most RAG and knowledge systems fail in production.
Fine-tuning and domain models
Foundation models are general-purpose; your problems are not. We fine-tune on your data, using LoRA, QLoRA, or full fine-tuning depending on the task and budget, so the model performs on your vocabulary, your document formats, and your edge cases. We measure the improvement with eval harnesses before and after, so "better" is a number, not a feeling.
Computer vision and document intelligence
Image recognition, object detection, OCR, and document extraction for logistics, manufacturing, and healthcare. Practical applications: defect detection on a production line, automated extraction of structured data from unstructured PDFs, and identity verification from document images, built on the right mix of open detection models and custom extraction pipelines for your accuracy requirements.
MLOps and production hardening
The engineering that keeps AI reliable after launch: eval harnesses that score accuracy, safety, and regressions on every release, observability so you can see what the model is doing in production, CI/CD so updates do not break what already works, and guardrails that catch out-of-distribution inputs before they reach users. This is the work most AI vendors skip, and it is what determines whether your system is still accurate in six months. Our AI infrastructure and MLOps practice covers it end to end.
What we have shipped
We have delivered 40+ production AI systems across 14 regulated and operational industries, with a 6.2x median return on AI investment. The engagements below are representative of the work; detailed case studies are available, with client-specific numbers shared under NDA on request.
Document intelligence for an insurance operator
Inbound claims documents, policy forms, adjuster reports, and medical records arrived in inconsistent formats from multiple sources. We built an extraction pipeline with a fine-tuned model and a validation agent that cross-references extracted fields against the claims system. Document processing time dropped 74 percent and manual exception handling fell 61 percent, deployed in ten weeks and running at 2,500+ documents per day.
Agentic dispatch for field operations
A field operations team was manually matching work orders, crew availability, and equipment location across dozens of daily assignments. We built an agentic dispatch system with human-in-the-loop checkpoints for high-risk assignments. Dispatch coordination time fell 68 percent and same-day completion improved by 22 percentage points, shipped in twelve weeks in the client's own cloud.
Clinical documentation assistant for a health network
Referral and prior authorization workflows were consuming three to five hours of clinical staff time per case. We built a retrieval-grounded generation assistant trained on approved clinical criteria and payer policy documents. Documentation time fell from 3.8 hours to 44 minutes per case and first-submission approval rates improved 19 percentage points, delivered in eleven weeks under a privacy-compliant architecture.
What production-grade actually means
Most AI vendors ship a working demo and call the project done. We hold every build to a production standard, because the difference between a demo and a system is everything that happens after launch. Before we hand anything over, a custom AI system has to clear all of the following:
- An eval harness with release gates. Accuracy, safety, and regression scores run on every change, and a release that fails its gate does not ship. Quality is a number your team can track, not a vibe.
- Observability in production. Traces, latency, cost per call, and output quality are monitored continuously, so you know the moment something drifts instead of hearing it from a user.
- Guardrails and fallbacks. Out-of-distribution inputs, prompt injection attempts, and provider outages are caught and handled by design, with sensible degradation instead of silent failure.
- CI/CD for models and prompts. Changes move through the same automated pipeline as the rest of your software, with rollback as a first-class operation.
- Cost and latency budgets. Routing, caching, and model selection are tuned so the system stays affordable and fast as usage grows, not just on launch day.
- Documentation and handover. Architecture docs, runbooks, and model documentation your engineers can operate from, because a system only you can run is a liability, not an asset.
Our custom AI development process
Four phases, fixed scope, no surprises. The same structure for every engagement, because the biggest risk in enterprise AI is ambiguity about what you are building and what done means.
01. Scope and align, one to two weeks
Working sessions with your team to map the highest-leverage problem, the data behind it, and the constraints that will shape the architecture. The output is a scoped brief: problem, data, success criteria, and a baseline to measure against. If we cannot agree on what done means here, we do not proceed to design.
02. Assess and design, two to three weeks
Data audit, model selection, integration mapping, and eval framework definition, reviewed with your team before a line of code is written. This is where the problems that kill AI projects get caught: data quality gaps, missing labels, infrastructure constraints, and compliance exposure. Better to find them in week three than week twelve.
03. Build and deliver, four to sixteen weeks
Iterative builds with eval gates on every release, CI/CD and observability from day one, and your team alongside the whole way, never handed a black box at the end. Everything runs in your cloud, under your security perimeter, against the success criteria we agreed in scoping.
04. Operate and hand over
We harden, document, and hand over. Your team owns the code, the models, and the runbooks, and you choose what happens next: run it independently, hand it to your internal team, or keep us on through managed AI services for monitoring, incident response, and continuous improvement.
How much does custom AI development cost?
Cost depends on scope, data readiness, and complexity. A well-scoped engagement with clean, accessible data and a clear success metric costs less and ships faster than an open-ended exploration over raw, unstructured data. We do not run open-ended retainers: every engagement is fixed-scope with transparent pricing agreed upfront, and if an AI readiness assessment shows you are not ready to build yet, we tell you that before you spend.
| Engagement | Typical range | Timeline |
|---|---|---|
| Readiness assessment / scoping | $15k - $35k | 1-3 weeks |
| Proof of concept / MVP | $40k - $100k | 4-8 weeks |
| Production system | $100k - $350k | 8-20 weeks |
| Enterprise platform | $350k+ | 20+ weeks |
These are engineering engagements, not staff augmentation or time-and-materials billing. You know the scope and the price before we start, and everything delivered is owned by you.
Who builds your system
No account managers, no offshore handoffs: the engineers who scope your project build your project. arosplatforms was founded by Rohan Bhanot, who leads product and engineering, and Adnan Mahmood, who leads delivery and partnerships. Together they have shipped production AI across financial services, healthcare, energy, logistics, and the public sector, and they stay hands-on through every engagement.
The wider team is senior by design. Every build pairs an architect who owns the system design with delivery engineers who own the eval harnesses, integrations, and hardening, and you work with them directly, from the first scoping call to handover. Meet the team on our company page.
Why teams choose custom AI development over off-the-shelf
Off-the-shelf AI products are built for the average case, which means they are wrong about your data formats, your compliance constraints, and your edge cases in ways you discover only after buying. Custom AI development services fit the system to the problem instead: your workflows define the architecture, your data trains and grounds the models, and your success criteria define the eval suite that every release must pass.
Custom also compounds. Because you own the code and the models, each system becomes infrastructure the next one builds on, the document pipeline feeds the agent, the agent feeds the analytics, and none of it is rented. If you are still deciding where AI fits, our AI strategy and advisory practice maps the highest-leverage use cases first, and our enterprise AI solutions show what those systems look like by workflow.
Custom AI Development for your industry
Deep-dive pages with sector-specific use cases, delivery steps, and FAQs.
Tools we work with
Frequently asked.
A custom AI development company designs, builds, and deploys AI systems around your specific workflows, data, and constraints, rather than selling you a generic product. That covers scoping the use case, engineering the system, evaluating it against your success criteria, integrating it with your stack, and hardening it for production. We take engagements from first scoping through deployment, with your team owning every deliverable.
Build AI that ships and stays shipped
Book a working session and we'll map Custom AI Development to your operation, then move fast.