We build grounded LLM applications, RAG systems, and AI copilots that answer from your documents, draft in your voice, and work inside your systems. Every output shows its source, and a human approves anything sensitive before it reaches a customer.
Prefer email? Write to [email protected] and describe one workflow you want to improve.
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Your team checks the source in seconds, then approves, edits, or rejects the draft.
Many generative AI tools write fluently and get the details wrong. We build the checking step in from the start, so your team trusts the output enough to use it every day.
A generic tool answers from what it learned in training, not from your policies, prices, or records. The draft sounds polished, but nobody can trace a figure to a source, and nobody knows what changed when the model updates. Staff stop trusting it, check everything by hand anyway, and the tool quietly falls out of use. Worse, a confident wrong answer can reach a customer before anyone notices, and fixing that mistake costs more time than writing the draft by hand did.
We provide generative AI development services that start with your content. The system retrieves the right passages first, writes from them, and cites each claim, so a reviewer can verify an answer in seconds. Guardrails block unsafe prompts and outputs, and human-in-the-loop review sits on anything that affects a customer, a payment, or a legal record. When no source supports an answer, the system says so plainly instead of guessing. That is the difference between a demo and a tool your team can rely on.
This approach shapes every project we take on. We scope the workflow first, prove the system on your real questions, and only then scale it. If your data is too thin, or a simple search tool would serve you better, we say so in discovery, before you spend on a build you do not need.
Pick one service to start, or combine them. Each one uses the same grounded retrieval, guardrails, and review controls.
We build LLM applications around a real workflow, not a generic chat window. That includes knowledge assistants for staff, customer facing helpers that stay inside approved content, and tools that draft, summarize, and search. We work with OpenAI GPT, Anthropic Claude, Google Gemini, and Meta Llama, and we route each task to the model that fits its accuracy, speed, and cost.
We build RAG systems, short for retrieval-augmented generation, that let a large language model answer from your documents and databases instead of guessing. Your content is turned into searchable meaning and stored in a vector database such as Pinecone, Qdrant, or pgvector. When a question arrives, the system finds the right passage first, then writes an answer with citations you can check.
We build AI copilots and assistants that sit inside your product or your internal tools, so staff get help without switching apps. A copilot can find a policy answer, draft a reply, or summarize a long thread, then hand the draft to a person for approval. The assistant follows your tone and your rules, and it only retrieves content the user is already allowed to see.
We build tools that read contracts, invoices, forms, and reports, then extract clean, structured data your systems can use. Each field is checked, low confidence results are flagged for human review, and an audit trail leads back to the source page. That turns document heavy work into a fast, checkable process instead of a typing task that eats whole afternoons.
We build multimodal AI development services that work across text, image, and voice, so one workflow can handle a photo, a scan, or a call transcript. A support team can search a manual from a photo of a part, or summarize a recorded call into next steps. Each input type is checked the same way, with sources shown and a human review step where the stakes justify one.
When prompting and retrieval are not enough, we adapt a model to your domain through fine-tuning. Using methods such as LoRA, we teach a foundation model your terms, tone, and formats, so drafts sound like your team wrote them. Fine-tuning suits teams whose language is specific, such as healthcare, legal, finance, or insurance, where a generic model keeps missing the mark.
Start where the work repeats and the answer matters. These are the project types our clients most often begin with.
Staff search policies, guidelines, and case files all day, and a wrong answer has real consequences. We build grounded assistants that cite the exact passage, keep access tied to role, and log every retrieval. That gives regulated teams a tool they can defend in review, not just a faster way to guess.
Underwriters, claims staff, and support teams work from dense documents with strict rules. Our enterprise generative AI development services help them find the right clause, draft a consistent reply, and flag anything uncertain for a person. Access controls and audit logs support the reviews your compliance team already runs.
Large catalogs, manuals, and ticket histories are hard to search by hand. We build assistants that answer product and policy questions from your own content, draft listings and support replies in your brand voice, and keep answers consistent across teams and shifts. Customers get faster, steadier answers, and staff spend less time hunting.
Support teams repeat the same answers, summaries, and follow ups every day. A copilot drafts the reply, pulls the source, and waits for approval, so agents keep the final say while the routine writing is already done. Managers can spot repeat questions early and fix gaps in the underlying content.
Product teams that want search, drafting, or summarization inside their own software get a custom build that matches their product and brand. We design the feature around your roadmap and your users, with guardrails that keep output on brand and costs visible per task, instead of a generic plugin that looks the same everywhere.
Companies with years of policies, manuals, research, and tickets gain the most from genai development services. If staff already search internal content all day, a grounded assistant cuts that search time and keeps one trusted version of the answer across offices and shifts. New staff find answers without waiting on a senior colleague.
Both approaches improve a model, but they solve different problems. Discovery tests your real questions against each one before you commit.
| What to compare | RAG system development | Fine-tuning a model |
|---|---|---|
| Best when | Answers must come from documents that change often | The model must learn your tone, terms, or formats |
| How it works | Retrieves passages first, then writes from them with citations | Adapts a foundation model on your examples, using LoRA |
| Updating knowledge | Add or edit a document, and answers follow at once | Needs a new training run when knowledge changes |
| Traceability | Each claim can link to a source passage | Output style improves, but sources still need retrieval |
| Cost shape | Running cost per question, tracked per task | Upfront training effort, then steadier running cost |
| Our advice | Start here for most business tools, because it is checkable | Add it when retrieval alone still misses your voice or terms |
In practice, many projects combine the two. Retrieval keeps answers grounded and current, while light fine-tuning makes drafts read like your team wrote them. We also build generative AI integration into the tools you already run, so staff never switch apps to get help, and we use frameworks such as LangChain and LangGraph to keep the system replaceable as models improve.
Security is not a feature we add at launch. We set role based access so staff only retrieve content they are already allowed to see, and we keep audit logs of what was retrieved, what was generated, and who approved it. Guardrails block unsafe prompts and outputs, and human-in-the-loop review sits on any step that affects a customer, a payment, or a legal record. Data residency follows your rules, so content stays inside the environment you approve.
For regulated work, we design with recognized guidance in mind, including NIST AI RMF for risk management and ISO 42001 for AI management systems, and we support readiness reviews for HIPAA, GDPR, SOC 2, ISO 27001, and the EU AI Act alongside your legal and security teams. That wording matters: we build the evidence and controls your reviewers need, and final compliance decisions stay with your own legal, security, and compliance reviewers. You also own the code, prompts, retrieval setup, and test sets, so you are never locked into one vendor model or platform.
Four phases, with a usable result and an approval point at the end of each one.
We map your workflows, data, and systems, then rank candidate uses by value, data readiness, and risk. If generative AI is the wrong tool, we say so plainly. You leave with a fixed scope and price for the pilot and a clear success measure.
We build a narrow pilot grounded in your content, with retrieval, guardrails, and review gates from the start. Your team tests it on real questions and checks citations. Nothing moves forward until the pilot meets the accuracy and cost bar you agreed.
We turn the pilot into a production system and connect it to your CRM, ERP, or document tools. Fine-tuning and multimodal inputs are added only where they earn their place. Every release is tested against your evaluation set, so a model update cannot quietly lower quality.
We launch to a controlled group first, watch accuracy, cost, and user feedback, and fix weak spots before a wider rollout. After launch, we keep evaluating outputs, refreshing content, and tuning prompts and retrieval as your documents and rules change.
No vague solution. A working tool, documentation, and a team that knows how to run it.
You receive a deployed system with retrieval pipelines, model and prompt configuration, integrations, and monitoring, running in your approved environment. It scales with demand and can switch models as quality and prices change, without a rebuild.
You receive an evaluation setup that tests accuracy on your own questions, tracks drift over time, and reports running cost per task in plain terms. Your team can see whether the system is getting better or worse after each model or content change.
You receive clear documentation, admin controls, and training for the people who will run the system day to day. Your team owns the code, prompts, retrieval setup, and test sets, and can extend the system and manage access without waiting on us for every small change.
You receive support after launch covering prompt tuning, retrieval fixes, content refreshes, and integration updates as your tools evolve. The goal is a system that stays trustworthy month after month, not one that slowly drifts while nobody is watching.
We start with a free consultation and a short discovery, then give you a fixed scope and price for the first phase. Custom generative AI development services vary with workflow, data, and integrations, so a fixed price before discovery would be a guess, and we do not sell guesses. A narrow pilot keeps the first commitment small and ties the next phase to proven results. You approve scope, price, and success measures before full build starts, and you own everything we build for you, including code, prompts, retrieval setup, and tests.
If the pilot shows that generative AI is not the right fit, we tell you, and we point to the simpler tool that would serve you better. That honesty is part of the engagement, not an extra. Book the consultation, bring one workflow, and leave with a clear next step either way.
We provide custom generative AI solutions built around one of your real workflows. That can mean an LLM application, a RAG system, an AI copilot or assistant, document processing automation, or a multimodal tool. Every build includes grounded retrieval, guardrails, human review gates, documentation, and support after launch, so the tool stays useful and checkable.
We ground answers in your own content first. The system retrieves the relevant passages, then writes from them, and shows the source next to each claim. If no passage supports an answer, the system says so and routes the question to a person. Human review gates sit before any customer facing output, and we keep testing accuracy on your real questions after launch.
Your data stays inside the environment your rules require, which can be your own cloud or a private setup. Role based access limits retrieval to content each person is already allowed to see, and audit logs record what was retrieved and approved. We design for HIPAA, GDPR, SOC 2, ISO 27001, ISO 42001, and EU AI Act readiness together with your legal and security teams, who make the final compliance decisions.
We work with OpenAI GPT, Anthropic Claude, Google Gemini, and Meta Llama, and we choose per task based on accuracy, speed, and running cost. Our stack includes LangChain, LangGraph, Pinecone, Qdrant, and pgvector, so the system can change models later without a rebuild. We explain each choice in plain terms before you approve it.
After a free discovery call, you receive a fixed scope and price for the first phase. We do not list prices on this page because scope, data, and integrations change the cost. A narrow pilot costs far less than a full enterprise rollout, and we track running cost per task so there are no surprises.
Book a free consultation or write to [email protected] and describe one workflow where your team loses time to searching, drafting, or document work. We review the use case honestly, outline a pilot scope with a fixed price after discovery, and show what success looks like before any larger build begins.
Start with one workflow, not a platform bet.
Book a free consultation, or write to [email protected]. Bring one problem, and leave with a scoped pilot plan.