Data & AI Practice
Generative AI Services
Generative AI can draft, summarize, answer and assist across the enterprise, but only when it is grounded in your own trusted content and surrounded by proper controls. Erpvora builds generative AI solutions using retrieval, prompting, guardrails and evaluation so outputs are useful, accurate enough for the task and safe to deploy.
Design and build generative AI solutions grounded in your own content, with retrieval, guardrails and evaluation for enterprise use.
The business challenge
Off the shelf chat tools impress in demos and disappoint in production. They invent facts, leak into areas they should not, and give different answers to the same question, which is unacceptable for enterprise processes that need consistency and accountability.
The harder issues are data and control. Grounding models in internal content without exposing sensitive information, measuring whether answers are actually correct, and keeping cost predictable are where most generative AI efforts struggle.
Our approach
We ground generation in your content using retrieval, so the model answers from approved sources and can cite them, rather than relying on its training alone. This sharply reduces fabrication and keeps answers current as content changes.
We wrap the solution in controls: access scoped to the user, guardrails on inputs and outputs, evaluation against real questions, and monitoring of quality and cost. We design for the specific task rather than building a general chatbot and hoping.
Capabilities
- Retrieval augmented generation over enterprise content
- Prompt design, orchestration and tool use
- Guardrails on inputs, outputs and access scope
- Evaluation frameworks for accuracy and quality
- Cost, latency and model selection optimization
- Integration into existing applications and workflows
How we deliver
- 01
Define the task
We pin down the specific task, the sources of truth and what a good answer looks like.
- 02
Ground in content
We build retrieval over approved content so answers are grounded and citable.
- 03
Add controls
We scope access, add input and output guardrails and handle sensitive data carefully.
- 04
Evaluate
We test against real questions, measuring accuracy and quality before release.
- 05
Deploy and monitor
We integrate into workflows and monitor quality, cost and latency in production.
Typical use cases
- Answering staff questions from internal policies and documentation
- Drafting and summarizing documents grounded in approved content
- Assisting support teams with suggested, sourced responses
- Searching and synthesizing across large document collections
- Extracting structured information from unstructured text
- Embedding a grounded assistant inside an existing application
Business impact
- Answers grounded in your content, with sources
- Far less fabrication than ungrounded chat tools
- Access scoped so users see only what they should
- Measured accuracy through evaluation before launch
- Predictable cost and latency in production
- Generative AI fitted to a task rather than a generic bot
Frequently asked questions
How do you stop the model making things up?
By grounding it in your approved content through retrieval and constraining it to answer from those sources with citations, rather than relying on its training alone.
Will our data be exposed or used to train public models?
We design access scoping and data handling so sensitive content stays controlled, and we select deployment options that keep your data out of public training.
How do you know the answers are good enough?
We build an evaluation set from real questions and measure accuracy and quality against it before release, then keep monitoring in production.
Which model should we use?
We select based on the task, accuracy needs, cost and deployment constraints, and we stay able to switch as the landscape changes rather than locking in.