Generative AI Development Services

PS3G is a generative AI development company that builds large language model applications for organizations of every size: assistants that answer from your own documents, copilots inside the software your staff already use, workflows that read, summarize, extract and draft, and content pipelines with a person in the loop. A founder might need a generative AI feature inside a product. A growing company might want a knowledge assistant over policies and contracts. A department in a larger organization might need document processing that respects existing permissions and leaves an audit trail. We build the application, choose the model with you, measure it against a golden set before and after launch, and put the governance in place that keeps it running.

Generative AI that ships, with the governance to keep it

A demo of an LLM answering questions takes an afternoon. A system that answers correctly from your documents, refuses what it should refuse, logs what it did, stays within budget and keeps working when the model vendor changes versions takes engineering. The gap between the two is where most generative AI pilots stall. Our generative AI development services are built around closing that gap: retrieval that is permission-aware, evaluation that is written down, guardrails that are tested, and cost that is measured per task from the first week.

Governance is part of the build rather than a later review. Each application ships with a model inventory entry, a risk note, logging and the human-review points that our AI governance practice recommends, so your compliance team sees a controlled system rather than a surprise.

What we build

• RAG assistants. Retrieval-augmented generation over your documents, tickets, wikis, contracts or product data, with citations, permission-aware retrieval and a refusal path when the answer is not in the sources. RAG development is the most common request and the one with the most ways to go wrong, from chunking to stale indexes.
• Copilots inside your applications. Assistants embedded in your CRM, ERP, helpdesk, portal or custom software that draft, summarize, look things up and take approved actions, with the user’s identity and permissions. Agents that act across systems are covered by our AI agents and chatbots service.
• Document workflows. Classification, extraction of fields from invoices, applications, forms and correspondence, comparison against records, and routing of exceptions to a person with the reasons attached.
• Content generation with review. Drafting of product descriptions, support replies, reports, proposals and marketing copy from structured inputs and approved sources, always with an editing step and a record of what was generated.
• Summarization. Meeting notes, call transcripts, long documents, case files and email threads reduced to what the reader needs, with links back to the source.
• Extraction and structuring. Turning unstructured text into database records, JSON for downstream systems, or tags and metadata for search.
• Custom LLM solutions. Fine-tuning or adapting a model for a narrow task where prompting and retrieval are not enough, with a clear case for why; our guide to RAG vs fine-tuning explains when that is justified.

Model choices and how we pick

We are vendor-neutral. The model is a component, chosen for the task, the data sensitivity, the deployment constraints and the cost, and designed to be replaceable.

Option	Where it tends to fit	What to check
OpenAI API	Fast start, broad capability, strong tooling for function calling and structured output	Data handling terms, regional availability, cost at your volume
Azure OpenAI	Organizations standardized on Microsoft that want the models inside their Azure tenancy, identity and network controls	Model availability by region, quota, integration with Entra ID and Purview
Google Gemini (Vertex AI)	Google Cloud estates, long-context document work, multimodal inputs	Data residency, grounding options, cost at your volume
Anthropic Claude	Long documents, careful instruction following, agentic workflows with tools	Availability through your cloud provider, cost at your volume
Open-weight models (Llama, Mistral, Qwen and others)	Strict data residency, on-premises deployment, high volume where hosting cost beats API cost, narrow fine-tuned tasks	Hosting and operations effort, capability gap on your task, licensing

In practice many applications use more than one: a small, fast model for routing and classification, a larger model for the hard cases, and a fallback if a provider is unavailable. We put a thin abstraction between the application and the model so that switching is a configuration change, and we evaluate every candidate on your own test set rather than on published benchmarks, which rarely resemble your documents.

Data, privacy and evaluation

LLM application development is mostly data and measurement work. The model is the smallest part.

Data

We inventory the sources, decide what the application may and may not see, map permissions from the source systems to the retrieval layer, and set rules for personal and regulated data: masking before it reaches a model, no training on your data, retention limits on logs, and regional deployment where residency matters. For healthcare work, the controls come from running our own HIPAA-regulated software.

Golden sets

Before the first prompt is tuned we build a golden set: real questions or documents with agreed correct outputs, written with the people who do the job today. Every change to the prompt, the retrieval, the chunking or the model is run against it, and the results are tracked over time. This is how we tell a real improvement from a lucky demo, and how you can tell whether a model upgrade is safe.

Guardrails and logging

Input and output checks for sensitive data, prompt-injection defenses for untrusted content, schema validation on structured outputs, a refusal path when retrieval finds nothing, limits on tokens and tool calls, and logging of prompts, sources, outputs and costs with the identity of the user. Logs feed the evaluation loop and the audit trail.

Scope a pilot around one use case and a golden set.

From pilot to production in stages

1. Use case and readiness. One use case with a measurable outcome, the data it needs, and the people who will judge it. Our AI consulting team runs this as a short assessment when the use case is not yet clear.
2. Pilot. A working application on real data for a small group, with the golden set, logging and cost tracking in place from the start.
3. Evaluation. Scores against the golden set, user feedback, cost per task, failure review. A written go or no-go.
4. Hardening. Guardrails, permissions, monitoring, fallbacks, rate limits, load testing by our testing center of excellence.
5. Production rollout. Staged release, training for users, runbooks for operations, and a governance record.
6. Operation. Monitoring of quality drift and cost, index refreshes, model upgrades tested against the golden set before they go live.

The broader AI engineering work (data pipelines, integration, MLOps) that production systems depend on is covered by our AI development service, and predictive models by machine learning development.

Engagement models

• Fixed-scope project. A pilot for one use case, with a golden set, an evaluation report and a fixed-scope quote, followed by a production phase if the numbers hold.
• Dedicated team or staff augmentation. LLM, integration and evaluation engineers working in your team on a monthly basis.
• Managed services. We operate the applications under defined SLAs: monitoring, evaluation, index refreshes, model changes and cost reporting.
• Advisory. Generative AI consulting: use-case selection, architecture review of an application your team is building, or a vendor evaluation.

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

Why PS3G as your generative AI development company

• We ship generative AI features inside our own HIPAA-compliant EHR and telehealth platform, DocVilla, so permission-aware retrieval, audit logging and cost control are things we operate daily under regulation.
• Over a decade of software development and IT services, with a US headquarters in Wilmington, Delaware and a delivery center in India.
• Microsoft-certified and open-source-certified engineers, so Azure OpenAI and open-weight deployments are both available to you.
• A testing center of excellence that builds and runs the evaluation suites, and a governance practice that keeps the application defensible after launch.

Related services

• AI development: the engineering behind every production AI system.
• AI agents and chatbots: assistants that take actions across your systems.
• AI consulting: use-case selection and readiness.
• AI governance: inventory, risk assessment and policy.
• Machine learning development: forecasting, classification and anomaly detection.
• All PS3G services: consulting, development, testing, cloud, data and AI.

Guides on this topic

• Generative AI Use Cases for Business: 25 Examples That Work Today
• AI vs Machine Learning vs Generative AI, With Business Examples

Frequently asked questions

What does a generative AI development company actually deliver?

A working application, not a demo: the retrieval layer over your data, the prompts and model configuration, the guardrails, the integration with your systems and identity, the user interface or API, the evaluation set and results, logging and cost reporting, and the documentation your operations and compliance teams need. We also deliver the decision record for the model chosen and how to switch it.

Which LLM should we use?

The one that scores best on your own test set at a cost you can sustain, within your data-handling constraints. We usually evaluate two or three candidates (for example Azure OpenAI, Gemini, Claude or an open-weight model) on your golden set and present the results. The application is built so the model can be swapped by configuration, because the best choice changes over time.

Will our data be used to train the model?

Not under the configurations we deploy. Commercial API terms from the major providers exclude training on customer data for business accounts, and we verify the setting rather than assume it. For stricter requirements we deploy inside your cloud tenancy or host an open-weight model on infrastructure you control. Logs are retained under your policy and masked where they contain personal data.

How do you measure whether the application is accurate?

With a golden set built with your subject-matter experts: real inputs and agreed correct outputs. Every change is scored against it automatically, and failures are reviewed by a person. We report the scores, the cost per task and the failure categories before go-live and at intervals after. We do not quote generic accuracy figures, because they depend entirely on your data and task.

What does RAG development involve?

Connecting to your sources, cleaning and chunking the content, building an index with embeddings, mapping source permissions into retrieval so users only see what they may see, writing prompts that answer from retrieved passages with citations, handling the case where nothing relevant is found, and refreshing the index as sources change. Most of the quality comes from the retrieval side rather than the model.

How is generative AI consulting different from development?

Consulting answers which use cases are worth doing, whether your data is ready and what the risks are, and produces a plan. Development builds and operates the application. Many clients start with a short consulting assessment and move into a fixed-scope pilot; others arrive with a clear use case and go straight to the build. Both are available on their own.