In AI, it’s easy to get lost in models. But real accuracy doesn’t come from the model alone — it comes from the data you feed it.
At Helpforce, we work with enterprises that need precision search, retrieval, and reasoning inside their own systems. Whether it’s internal chat, policy documents, SOPs, or client history — we help vertical AI systems understand your world, not the internet.
The secret? Domain-specific embedding models.
And the key to training them? Clean, curated, context-rich data.
If you’re building an internal AI assistant, you’re not asking it to Google things.
You want it to find relevant answers from your data.
That’s what embedding models do — they turn your internal knowledge (docs, chats, logs) into a format that can be searched, ranked, and retrieved instantly with high accuracy.
But here’s the catch: generic embeddings fall short in specialized environments.
You need models that are trained to speak your domain’s language.
It starts with better data.
Here’s how we prep data for fine-tuning embedding models:
Remove repetitive entries that look identical — or almost identical — to avoid polluting the training set.
We use embeddings themselves to find near-duplicate meaning. For example:
“What’s your return policy?”
“How do I send something back?”
Both say the same thing — one can go.
We filter out incomplete chats, hallucinated answers, off-topic logs, or poor grammar that weakens learning.
Custom rules to catch edge cases — like when agents copy-paste irrelevant boilerplate, or when messages are empty, noisy, or broken.
Where real-world data is limited (like rare error cases), we generate synthetic examples using AI — always controlled and reviewed.
Fine-tuning with clean, domain-tuned data improves performance across the board:
It’s not just about adding more data. It’s about curating the right data.
We don’t just build AI.
We build vertical AI — trained for your world, not the general web.
As NVIDIA Inception members, we use best-in-class tools to help you:
Let’s train it right from the start.
