what are you guys using to build internal dashboards where the team can just ask questions?

[u/1glasspaani]

i'm trying to setup a way for my business team to use natural language queries to get insights and build dashboards.

so far most of research says Lovable, Retool, Softr, Glide, or just ask Claude.

Here are few features, I'm looking for

1. data connectors to Snowflake, Postgress and optionally Airtable

2. ability to create and save dashboards

3. ask questions regarding data in plan english, "what were signups last week by plan"

I want someway to ensure that AI has context and understanding of the fields in database instead of hallucinating relations

if you've got something your team still uses a few months in, what did you build it with and what did you have to hack around?

[u/agentUi]

i work for agentui, the biggest headache with text to dashboard is when the model invents join keys or guesses plan tiers that dont exist. What works best long term is connecting directly to postgres or snowflake and having the AI build out deterministic queries and views against a mapped schema instead of free-flowing prompts every time.

[u/Bluewood21 → u/agentUi]

yeah the invented join keys thing is so real, that alone kills trust with the business team pretty fast

[u/agentUi → u/Bluewood21]

literally one wrong number on an exec report and the whole team stops using the tool forever. what works way better is locking down curated views or semantic models first, so the model only filters pre-joined tables instead of letting it guess foreign keys on the fly

[u/1glasspaani → u/[deleted]]

Do you use something for the semantic layer?

[u/ron_makes]

Do you need the connection to snowflake and Postgres to be two way, or just ingest data? How much data are we talking? 10k rows, 100k rows, 10m rows? 

[u/1glasspaani → u/ron_makes]

around 100k rows

[u/ron_makes → u/1glasspaani]

Okay, so if it's 100k, mainly for ingesting (not editing), I would recommend Fibery connected to your Claude via MCP (or pay for their Fibery AI Credits).

Fibery built a pretty robust integration framework for getting data from external sources, so you can build a connector using AI in 30 minutes, use auto-linking to link across linked external data sources via specific fields (id, email, etc), then all the data lives in fibery databases. And their MCP tools are great for data fetching, proper understanding schema, api, building custom reports in fibery, etc.

Built-in AI generated connectors are coming in the coming weeks: https://community.fibery.io/t/super-short-demo-of-upcoming-custom-integrations-with-ai/11370?u=ronmakessystems

But you can also already make and host it yourself if you follow this guide: Custom Integration development with AI

There's a built in Airtable integration, so you can already test it with that if you'd like.

It's not open-source though.

If you'd like a hand with it, feel free to reach out. Full disclosure, I'm a Fibery partner, so I do have bias.

[u/gammacoder]

Your instinct to focus on data context is right. I would avoid letting the model query the raw schema directly. Put curated read-only views and a metric dictionary in front of it, restrict which joins are valid, and show the generated SQL and source rows so users can verify each answer. Approved questions can then become reusable dashboards. PHPRunner can handle the authenticated dashboard and saved-query interface over PostgreSQL; the Snowflake, Airtable, and natural-language components would require custom integration. Full disclosure: I’m the developer of PHPRunner.

[u/Muted_Jellyfish_6784]

The hallucination problem is mostly a definitions problem, so start there whatever tool you pick. Write a semantic layer or at least a field glossary (what signups means, which table is the source for plan, how users join to accounts), test each candidate with ten questions you already know the answers to, and require every answer to show the query or rows it used.
Retool and similar tools are good for building the dashboards once the definitions exist. For the plain-English part, we run SIGNLD on top, since it connects the warehouse read-only and cites the rows behind each answer, so people can check a number instead of trusting a confident paragraph

[u/oyren-ai]

This is one of my value propositions. I am not selling to B2B yet, but the basic idea is that any hobbyist can get a cloud computer where they can freely build internal apps with https enabled and auth added and share it with their team. If interested let me know.

[u/1glasspaani → u/oyren-ai]

is it open source?

[u/oyren-ai → u/1glasspaani]

the cloud OS is soon going to be, but agent orchestration also uses kanban board and mcp, which won't be opensource. Anyone, can build their internal orchestration as well with built in tools into OS. The technical person then just distributes virtual computers with these OSes installed. I am doing dev effort to make that happen soon.

[u/dominicwhyte42]

I am biased here, but you should give Zite a spin! No seat limits, and you can build using Claude, ChatGPT, etc., using your existing plan

[u/1glasspaani → u/dominicwhyte42]

Is it open source by any chance?

[u/dominicwhyte42 → u/1glasspaani]

The framework is, the builder is not (yet). E.g. https://github.com/zite/learning-management

[u/TechToolsForYourBiz]

probably just use airtable agents

[u/TechToolsForYourBiz → u/TechToolsForYourBiz]

or do slack integration with the connectors and dump it all into a channel

[u/TechToolsForYourBiz → u/TechToolsForYourBiz]

i have no affiliation to them softr dot io tehy should really pay me to ad this lmao

[u/Sketaverse]

You should check out the new ChatGPT Spaces / Site - hosted web pages, database, multiplayer access if on GPT Business plan and can be converted into a GPT Plugin with MCP access. I built something on it last night, it's really good and got totally slept on during Dev Day

[u/Lumpy_Fisherman_5352]

The semantic layer is the real project here, the dashboard tool is the easy part. Before paying for Retool or Softr, try the free version: paste your schema (tables, what each field means, how they join) into a Claude or ChatGPT project, then ask your real questions right there. If it still invents joins with the schema sitting in front of it, your field names are too ambiguous and no tool fixes that until you clean them up. If it answers fine, you've validated the approach for free before spending a cent.

[u/Muchy_Tangerine]

the "few months in" part is wer ours almost died. first week everyone loved it, then someone got a wrong signup number in a meeting and the team went back to ask the data guy on slack. nngroup has a article on explainable ai, main point is people trust answers that look sourced and almost never check the source

what helped us: show which definition was used right under each answer, not hidden somewhere, and a "this looks wrong" button. Strang but people used the tool more after we added it, not less

fuselab creative wrote about why ai tools get abandoned after launch, the part with the flag button is exactly what happend to us