AI Development Services for Custom Apps, Agents and LLMs
AI development services for mid-market teams that want custom AI apps, agents and private LLM builds on their own data. We design each system around your workflow, test it against your own examples and deploy it in your cloud account or on your own servers, with a handover your engineers can run.
Custom AI Development Built Around Your Data and Systems
Our AI engineering services cover seven kinds of build. A single project often combines two or three of them, such as an agent that reads documents and answers questions from your database.
AI Apps and Copilots
AI app development services for internal tools and customer-facing apps, such as a copilot inside your CRM or help desk, a drafting assistant for proposals or a review tool for analysts, built on your data and your sign-in.
AI Agents
Agents that plan multi-step work and call your APIs, databases and business systems, with a person approving the steps you choose.
Chatbots
Chatbots for your website, help desk or internal teams, grounded in your documents and systems, with a handoff to staff when a question needs a person.
Document Processing
Extraction from invoices, receipts, purchase orders, claims and intake packets into structured fields, with low-confidence fields sent to a review queue.
Private LLM Deployment
Open-weight models served with vLLM on your own GPUs or in your cloud account, behind an OpenAI-compatible API your apps can call with a URL and key change.
Data Analyst Agents
Plain-English questions turned into SQL on your own database, with the query and row count shown beside every answer so people can check the number.
Integrations
Connections to your CRM, ERP, help desk and file stores through APIs and MCP, the open-source standard for linking AI apps to tools and data, starting with read-only access.
When the work centers on generative models, such as fine-tuning a model on your documents, our generative AI team scopes it with you. If you would rather have engineers working inside your own sprints, we also provide embedded engineers for your team.
How AI Application Development Runs, From Discovery to Handover
Every project moves through the same six stages. Each stage ends with something your team can review, from a written scope to a working prototype, an eval report and a runbook.
Discovery
We map the workflow, the people who use it, the data it needs and how you will judge the result, then agree on a written scope.
Data Access
We connect to the systems in scope with read-only accounts first, record where each data source lives and set what the model may see.
Prototype
A working version runs on a sample of your real data, in front of the people who will use it, so their feedback shapes the build.
Evals
A test set built from your own examples scores accuracy, citations and refusals, and every model or prompt change must pass it before release.
Production
The system goes live in your cloud account or on your servers, with sign-in through your identity provider, logs, monitoring and review queues.
Handover
Code, prompts, evals and infrastructure config go into your repositories, with a runbook and training for the engineers who will own the system.
Planning an AI build on your own data?
Book a free strategy session. Bring the workflow you want to improve and the systems it touches, and we will outline the build, the data it needs and where it should run.
AI Software Development Services Deployed in Your Own Environment
Every system we build runs in your cloud account or on your own servers, so prompts, documents and model output stay on infrastructure your team already controls and audits.
In your cloud account
We deploy on AWS, Microsoft Azure or Google Cloud under your own account, region and identity provider. AWS's data privacy FAQ states that customers own their content and choose the Regions where it is stored. Hosted models can run through your cloud's own AI service. On Amazon Bedrock, model providers have no access to Bedrock logs or to customer prompts and completions, per AWS's Bedrock documentation. Microsoft states that prompts and completions sent to Models sold by Azure in Microsoft Foundry are unavailable to OpenAI and are used to train foundation models only with your permission or instruction.
Self-hosted on your servers
When data has to stay on your own hardware, we serve open-weight models with vLLM on GPUs you own or rent and run embeddings, vector search and logs beside them. Our self-hosted AI stack covers the full setup, from private chat to model serving. Many builds mix the two, and an LLM gateway routes each request to a hosted or self-hosted model and records cost by team.
Models: your cloud's AI service or open-weight models on vLLM in your account.
Data and embeddings: your storage and your vector database.
Logs and evals: your monitoring stack and your repositories.
Sign-in: your identity provider, with access set by role.
Choosing an AI Development Company for a Build on Your Data
Three common routes to an AI system, compared on what each one is designed for and where your data runs.
| SaaS AI tools | General software development shop | AI development partner that deploys in your account | |
|---|---|---|---|
| Designed for | A common task many companies share, such as meeting notes or writing help | Web, mobile and back-end software across many stacks, with AI added to a project as needed | AI systems built around one company's workflows, data and review steps |
| Where data and models run | The vendor's cloud, under the vendor's data terms | Wherever the project specifies | Your cloud account or your own servers |
| Fit to your workflow | Set through the product's settings and integrations | Built to the specification you write | Designed from your workflow, with your users testing the prototype |
| AI-specific work | Model choice and tuning handled by the vendor | Scoped per project | Model selection, retrieval, evals and guardrails as core build steps |
| Code and configuration | The vendor runs the product and your admins manage its settings | Set by the contract | Code, prompts, evals and infrastructure config in your repositories |
| Pricing model | Per-seat or usage subscription set by the vendor | Project or time-and-materials quote | A scoped quote for the build, with model and cloud usage billed by your providers |
| Best fit | A standard task the product already covers | Software projects where AI is one feature among many | Workflows on sensitive or proprietary data that need to run in your environment |
An AI software development company that deploys in your account suits work where the data is sensitive or the workflow is specific to your business. Many teams use more than one route, with SaaS tools for general tasks and custom builds for the workflows that set them apart.
What Shapes the Engagement
Custom AI development services are scoped around six factors: the workflow and who uses it, the data sources and their condition, the systems the build connects to, where it has to run, the review steps your team needs and who will own it after handover. We confirm each factor in the strategy session, then quote against that scope and answer schedule questions there.
Your answers set the scope.
We confirm each factor with you before we propose anything.
Frequently Asked Questions on AI Development Services
Our AI development services cover the full build: discovery and scope, data access, a prototype on your real data, an eval set built from your examples, deployment in your cloud account or on your servers and a handover with a runbook. The result can be an AI app, an agent, a chatbot, a document pipeline or a private LLM endpoint, and many builds combine several.
We quote each build after the strategy session, because the cost follows the scope: the systems it connects to, the condition of the data, the review steps and where it runs. Model and cloud usage is billed by your providers at their published rates. For example, AWS's SageMaker AI pricing page lists an ml.g5.2xlarge instance at $1.52 per hour in US East (N. Virginia) in its LLM hosting examples (checked November 2026).
Ask where the system will run and where the code will live, how accuracy is measured before each release, which steps a person reviews and how the team hands over to your engineers. A team that deploys in your account and builds its evals from your own examples gives you a system you can test, audit and run.
Yes. We deploy in your AWS, Azure or Google Cloud account and can call hosted models through services such as Amazon Bedrock or Microsoft Foundry under your account's data terms. Where data has to stay on your hardware, we serve open-weight models with vLLM, whose OpenAI-compatible API lets the same app code call a hosted model or a self-hosted one.
Code, prompts, eval sets and infrastructure config are committed to your repositories and deployed in your cloud account during the build, and the ownership terms are written into the statement of work. Your engineers get a runbook and training, and we can stay on for support if you want us to.
Talk to Us About Your AI Build
Book a free strategy session to talk through your workflow, your data and where the system should run. If you have a question first, send it through our contact page and we will reply.