AI Development Services

PS3G is an AI development company that builds artificial intelligence into the software you already run: the portal your customers use, the back office that processes documents, the CRM and ERP your team lives in. We design, build, integrate and operate custom AI solutions for organizations of every size, whether you are a founder shipping an AI feature in a first product, an operations lead at a growing company with a document backlog, or an IT director inside a larger organization who needs language models connected to internal systems under security review. The result works on your data, respects your permissions, is measured against a quality bar you set, and costs a known amount to run each month.

AI built into the software you already run

Most of the value in AI comes from integration, not from a chat window. A model that can read a customer record, check an order status, draft the reply and file the ticket saves time; a model that only talks does not. So our AI development services start from your systems rather than from a demo. We build in .NET, Python and Node.js, connect through the APIs you have (or build them through our API development and integration service), and ship the feature inside your existing web application, mobile app or internal tool.

The engagement can be a single feature or an entire AI product. A founder might need one capability, such as answering questions over product documentation. A growing company might need document intake connected to its ERP. A division of a larger organization might need a copilot inside a line-of-business app with single sign-on, audit logs and data residency. The patterns are the same; the scope and the controls are sized to you.

What we build

Document and email processing

Intelligent document processing that reads invoices, applications, contracts, claims, intake forms and inbound email, extracts the fields you need into a schema, classifies and routes the item, and sends low-confidence cases to a person. We combine OCR and layout models (Azure AI Document Intelligence, Google Document AI, Amazon Textract) with language models using structured outputs, so the result is validated JSON, not free text.

Search and Q&A over your data with RAG

Retrieval-augmented generation (RAG) lets a model answer questions from your policies, manuals, tickets, contracts or knowledge base with citations, without training on your data. A RAG implementation involves chunking and embedding your documents, a hybrid index (keyword plus vector, in Azure AI Search, PostgreSQL with pgvector, Elasticsearch or OpenSearch), permission-aware retrieval, reranking, and an evaluation set that checks answers are grounded in the sources.

Forecasting and scoring models

Predictive analytics services for demand forecasting, churn and lead scoring, cash-flow projection, anomaly detection and maintenance prediction. These are usually classical machine-learning models (gradient boosting, time-series methods) trained on your history, with pipelines from our big data and data engineering team and retraining scheduled as data drifts.

Computer vision

Computer vision development for defect detection on a production line, document and ID capture, inventory counting, safety monitoring and image search, using cloud vision services or open models fine-tuned on your images, deployed to the cloud or to edge devices.

Voice and chat interfaces

Speech-to-text, text-to-speech and conversational interfaces for phone lines, kiosks and apps, wired to the same tools and data as the rest of your system. Agents with tool access are covered on our AI agents and chatbot development page.

Copilots inside your apps

An assistant embedded in your own product or internal application that can summarize a record, draft the next action, fill a form from a conversation or explain a report, with the user’s permissions and a full audit trail. This is a common AI app development request from product teams.

Integration first: connecting models to your data and systems

AI integration services are where projects succeed or fail. The model is a small part of the code; the rest is data access, orchestration, permissions and error handling.

• APIs. REST or gRPC services wrapping the model, with request validation, rate limits, retries, timeouts and a fallback path when the provider is slow or down. Tool and function calling lets the model query your systems through typed interfaces; the Model Context Protocol (MCP) standardizes how those tools are exposed.
• Event pipelines. Queue-based processing (Azure Service Bus, Amazon SQS, Kafka) for high-volume work such as document batches, so spikes do not take down the application and every item is retried or dead-lettered, never lost.
• Permissions. The model only sees what the current user may see. We enforce document-level access control at retrieval time, pass identity through from your single sign-on, and apply row-level security on structured data.
• Data readiness. Clean, labeled, deduplicated data with a known owner. Where that is missing, our data governance service runs alongside the build.
• Secrets and configuration. API keys in a vault, per-environment model deployments, prompts under version control.

Model choices and when to fine-tune

We are vendor-neutral on models. The choice depends on where your data lives, your compliance obligations, the task and the budget; we usually test two candidates on your own evaluation set before committing.

Option	Typical fit	Notes
OpenAI API	Fast start, broad model range, strong tool calling and structured outputs	Review data-use and retention terms for your tier, plus region and residency needs
Azure OpenAI (Azure AI Foundry)	Organizations on Azure needing private networking, regional deployments, content filters and Microsoft data-handling commitments	Same model families as OpenAI, with Azure identity, logging and provisioned throughput
Google Gemini (Vertex AI)	Google Cloud and Workspace shops; long-context and multimodal tasks	Enterprise terms through Vertex AI; grounding with Google Search where useful
Anthropic Claude	Long documents, careful instruction following, coding and agentic workflows	Available directly and through major cloud marketplaces; check the data terms for your route
Open-weight models (Llama, Mistral, Qwen, Gemma, Phi)	Self-hosting for data control, offline use, or cost at high steady volume; fine-tuning for narrow tasks	You own the serving stack (vLLM or a managed endpoint), its patches and its capacity planning

When to fine-tune an LLM. Fine-tuning teaches a model a style, a format or a narrow classification task; it does not teach it your facts. If the goal is answers from your documents, RAG is the right tool. If the goal is consistent output in a strict format, lower latency, or moving a task from a large model to a smaller one, supervised fine-tuning (with LoRA adapters on open-weight models, or a provider’s fine-tuning API) can pay off. Training a custom LLM from scratch is rarely justified; a fine-tuned open-weight model is what custom LLM means in practice for most organizations. Our guide RAG vs fine-tuning vs custom LLM walks through the decision.

Evaluation, safety and monitoring (MLOps and LLMOps)

An AI feature without evaluation is a demo. Before launch we build a golden set of real inputs with expected outputs and score every prompt, model and retrieval change against it, in the same pipeline that runs your unit tests. Scoring combines exact checks where they exist, rubric-based grading, and model-graded evaluation calibrated against human reviewers.

• Tracing and observability. Every request is traced (prompt, retrieved context, tool calls, tokens, latency, cost) with OpenTelemetry-compatible tooling such as Langfuse, LangSmith or Azure AI Foundry tracing, so a bad answer can be reproduced.
• Safety controls. Content filters (Azure AI Content Safety, provider moderation endpoints, or open classifiers such as Llama Guard), prompt-injection defenses for untrusted documents, PII detection and redaction, and schema validation before anything is written to a system of record.
• Production monitoring. Dashboards for quality drift, refusal and fallback rates, retrieval hit rate, user feedback, latency and cost per task, with alerts and a human review queue.
• Change control. Pinned model versions, staged rollouts, a rollback path, and a record of what changed and why, which is also what your AI governance policy will ask for.
• Classical ML. Feature pipelines, a model registry, scheduled retraining and drift detection, on Azure Machine Learning, Vertex AI, Amazon SageMaker or MLflow.

Cost control

Run cost is a design decision, not a surprise on the invoice. Generative AI cost scales with tokens in and out, model tier, steps per task and retries, so we set a cost-per-task target during the proof of concept and design toward it:

• Route by difficulty: a small model handles the easy majority, a large model only the hard cases.
• Cache: provider prompt caching for long shared context, application caching for repeated questions.
• Batch what is not urgent: nightly document runs through discounted batch endpoints.
• Cap and budget: output length limits, step limits for agents, token budgets per user or feature, alerts on spend.
• Right-size hosting: pay-per-use while volume is uncertain, provisioned throughput or self-hosted open-weight models once it is steady.
• Use the simplest tool that works: a rule, a regular expression or a classical model where a language model is not needed.

Our process

1. Proof of concept. A timeboxed build against a golden set of your data, with cost-per-task and latency measured and a written go, change or stop decision. If you have not yet chosen a use case, our AI consulting team runs the readiness and prioritization work first.
2. Pilot. Real users, real data, limited scope, with integration, permissions, logging and the review queue in place. We measure override rate, time saved and user trust.
3. Production. Hardened integration, monitoring, runbooks, training for the owning team, and either hand-over to your engineers or a managed-service arrangement with us.

Request a fixed-scope proof of concept for the use case you already have in mind.

Engagement models

• Fixed-scope project. A proof of concept, a pilot or a single feature, with a defined deliverable and a fixed-scope quote.
• Dedicated team or staff augmentation. Hire AI developers, data engineers and an MLOps engineer who work inside your team month to month and scale with the roadmap.
• Managed services. We run the AI system you launched under defined SLAs: monitoring, evaluation, model upgrades, cost reporting and support from our US and India teams.
• Advisory. Machine learning consulting and MLOps consulting for a team building in-house that wants architecture review, evaluation design or a second opinion.

How we start: a first conversation, a short assessment, and a written proposal.

Why PS3G as your AI development company

• We ship AI features inside our own HIPAA-compliant EHR and telehealth platform, DocVilla, so we build, evaluate and operate AI under regulatory and privacy constraints as a product owner, not only as a vendor. The same discipline carries into our healthcare software development work.
• Over a decade of software development, with a US headquarters in Wilmington, Delaware and a delivery center in India for round-the-clock coverage.
• Microsoft-certified and open-source-certified engineers, so Azure OpenAI, Google, Anthropic and open-weight models are all options rather than a default.
• A testing center of excellence that treats AI evaluation as release engineering.
• Consulting, build, test, run and governance in one firm, so the people who designed the architecture are the people who operate it.

Related services

• AI consulting: readiness, prioritization and roadmap before the build.
• AI agents and chatbot development: tool-using agents and conversational interfaces.
• AI governance: inventory, risk assessment, policy and framework alignment.
• Data governance: quality, ownership and access rules for the data your models use.
• API development and integration: the interfaces between models and your systems.
• Web application development: the applications AI features ship inside.
• Big data and data engineering: pipelines for forecasting and scoring models. See all PS3G services.

Guides on this topic

• How to Implement AI in Your Business (Without Dead Pilots)
• AI Proof of Concept: How to Run One That Leads to Production
• RAG vs Fine-Tuning vs Custom LLM: Which Approach Fits Your Use Case?
• AI Agents for Business: Use Cases and How to Build One Safely
• Data Governance for AI: The Prerequisite Nobody Wants to Fund
• Generative AI Governance: Policies, Controls and Guardrails

Frequently asked questions

What does an AI development company do that a regular software developer does not?

The integration work overlaps, but AI development adds evaluation against a golden set, prompt and model versioning, retrieval design, safety filtering, cost-per-task engineering and production monitoring for quality drift. Language models are probabilistic, so the testing, release and operations practices differ from those for deterministic code. An AI software development company brings both disciplines: the application engineering and the machine-learning operations around it.

Do we need our own model, or can we use OpenAI, Azure OpenAI, Google or Anthropic?

Most organizations should use a hosted model from a major provider and spend their budget on integration, retrieval and evaluation. Self-hosting an open-weight model makes sense when data cannot leave your environment, when you need offline operation, or when steady high volume makes the serving cost lower than per-token pricing. Training a model from scratch is almost never the right answer for a business application.

What is RAG implementation and when do we need it?

RAG, or retrieval-augmented generation, retrieves the relevant passages from your own documents at question time and gives them to the model to answer from, with citations. You need it whenever the answers must come from your content: policies, manuals, contracts, tickets or product documentation. It avoids training on your data, keeps answers current as documents change, and lets you enforce who can see which source.

Can you fine-tune an LLM on our data?

Yes, when it is the right tool. Fine-tuning works for teaching a consistent format, tone or classification scheme, and for moving a task to a smaller, less expensive model. It is not a way to give a model knowledge of your business; retrieval does that better. We usually exhaust prompt design and RAG first, then fine-tune with a measured before-and-after on the same evaluation set.

Can we hire AI developers from PS3G for our own team?

Yes. Through staff augmentation you can add AI engineers, data engineers or an MLOps engineer to your team on a monthly basis, working in your tools and under your management, with the option to scale up or down. Many clients combine this with a short advisory engagement so the architecture and evaluation approach are agreed before the engineers start.

How do you keep our data out of public models?

By choosing routes with contractual data commitments (such as enterprise API tiers, Azure OpenAI, Vertex AI or cloud marketplaces), confirming that prompts and outputs are not used for training, setting retention to the minimum, keeping data in the required region, redacting personal data where it is not needed, and documenting all of it in your AI inventory. For the strictest cases we self-host open-weight models in your environment.