Doslr
AI-enabled systematic literature review
A research workspace for systematic literature reviews with references, notes, collaboration and an AI-enabled editor.
AI features and automation
We add AI where it measurably helps: retrieval-based search over your documents, assistants grounded in your data, drafting and review flows, and automation for repetitive internal work. Every AI feature ships with quality checks and human control, because model output is a draft until proven.
What you get
- AI features grounded in your own data, not generic answers
- Automation for repetitive work your team should not do by hand
- Quality checks and fallbacks around every model output
- A model setup you can swap or self-host as needs change
Approach
How we run it
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Find the real use
We look for flows where AI saves genuine time — search, drafting, review, support — and skip the rest.
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Ground it in your data
Retrieval, structure and context design come before prompt tricks, so answers stay specific to you.
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Keep humans in charge
Suggestions and drafts stay reviewable. Nothing critical ships unchecked.
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Measure and adjust
We watch quality and cost in production, then tune or remove what underperforms.
Where AI fits
If your data cannot leave your infrastructure, we deploy open models with Ollama or vLLM and vector search with Qdrant behind your own firewall — the same features, private by design.
FAQ
Common questions
Can you add AI to our existing product?
Yes, and it is the most common case. We integrate retrieval, assistants or automation into the product you already run — no rebuild required, and the feature respects your existing roles and data boundaries.
Which AI models do you work with?
Claude, GPT and strong open families such as Qwen and DeepSeek, served through APIs or self-hosted with Ollama and vLLM. We pick per use case on quality, cost and privacy, and design so the model can be swapped later.
How do you keep AI answers reliable?
By grounding them. Assistants answer from your data through retrieval, output is validated and shaped before users see it, and sensitive flows keep a human in the loop. We treat model output as a draft, not a fact.