Generative AI features that make it past the pilot
RAG search, copilots, and content engines built on your proprietary data, with evaluation, cost control, and human review designed in before the first demo.
How it works
Generative AI demos beautifully and dies quietly. The gap is almost always the same: no evaluation harness, no grounding in company data, no plan for the 5% of outputs that are confidently wrong. We build the scaffolding that turns a promising prototype into a system a business can rely on, retrieval over your content, structured outputs your code can trust, human review where the stakes justify it, and cost telemetry so finance never gets surprised.
Retrieval built for messy real data
We turn your contracts, tickets, wikis, and product data into a retrieval layer the model can reason over reliably.
- Chunking and metadata design tuned to your document shapes
- Hybrid keyword plus vector search with reranking
- Permission-aware retrieval so answers respect user access
- Freshness pipelines that reindex as source content changes
Copilots that live inside the workflow
Generation embedded where the work happens, producing output in the exact format your systems expect.
- Drafting, summarizing, and rewriting against your tone and templates
- Structured JSON output validated before it touches your database
- Human-in-the-loop approval on anything customer-facing
- Ships inside your product, portal, CRM, or Leadify workspace
Evaluation and cost control as first-class features
You get numbers, not vibes: accuracy scores before launch and continuous quality tracking after.
- Graded evaluation set built from your real examples
- Regression tests run on every prompt or model change
- Per-feature cost, latency, and token dashboards
- Caching and model routing to keep unit economics sane
Where teams use it
Built for real revenue work.
Ask-your-documents search
One question box across contracts, SOPs, past proposals, and support history, returning a cited answer instead of ten links. Typically saves knowledge workers three to five hours a week.
Content and proposal engines
Generate RFP responses, product descriptions, and campaign variants from approved source material, with brand rules enforced and a reviewer approving before anything ships.
Domain copilots for specialists
An assistant that drafts the clinical note, the underwriting summary, or the engineering change request, then routes it to the person accountable for signing off.
“Our internal prototype impressed everyone and then sat for eight months because nobody could prove it was accurate enough to trust. APPIT built the evaluation set first, and that's what got legal and the CTO to sign off.”
FAQ
Questions, answered.
What is retrieval augmented generation (RAG)?
RAG means the model answers using passages pulled from your own content at question time, rather than from what it memorized during training. Your documents are indexed, the most relevant pieces are retrieved for each question, and the model is instructed to answer only from those passages and cite them. It's how you get answers about your Q3 pricing policy or a specific customer contract, and it's far cheaper and faster to update than fine-tuning, because you just update the documents.
Do we need to fine-tune or train our own model?
Almost never at the start. Retrieval plus careful prompting solves the large majority of business cases at a fraction of the cost, and it stays current as your data changes. Fine-tuning earns its keep for narrow, high-volume tasks with a consistent output format, such as classification at scale or matching a very specific writing style. We'll tell you plainly which one your use case needs, and it's usually the cheaper one.
Who owns the code and the outputs?
You do. The repository, prompts, evaluation sets, retrieval pipeline, and infrastructure definitions are yours, delivered in your accounts under your license. There is no runtime that only we can operate and no per-seat AI fee. If you ever want to bring the work in-house, we hand over documentation and run knowledge-transfer sessions with your engineers as part of the project.
How do you keep it from producing confidently wrong answers?
We constrain the model to retrieved sources, require citations, and validate structured outputs against a schema before anything is written or displayed. Before launch we score the system against a graded set of real questions with known good answers, and we don't ship until it clears the bar you set. After launch, sampled outputs are scored continuously and any prompt or model change reruns the full regression suite.
Keep exploring
More from the Leadify platform.
Get your generative AI project out of the demo phase
Bring us the use case and we'll scope it, price it fixed, and show you a working prototype on your data.