Ask your data in plain English
Natural-language questions over your product's data and documents, with answers tied to the source.
AI product engineering · for software teams
AI features built to hold up with real users.
CoEdify designs and builds AI features for software products: natural-language search over your data, in-product assistants and agent workflows. It also handles the platform work under them.
First working version in two weeks. If it doesn't satisfy you, you don't pay for it.
A good fit if
- AI is on your roadmap, and your team is busy shipping everything else.
- You have a prototype that impresses in demos and struggles with real users.
- You can't tell whether the AI gives good answers once it's in production.
- You need the AI feature built now, while you hire.
What we build
In-product assistants
Assistants that understand your users' context and can take actions inside your product.
Agent workflows
Multi-step AI tasks with approvals, retries and a clear record of what happened.
Evaluation
Tests and scoring that show whether answer quality holds up, before your customers tell you.
The platform around it
APIs, data pipelines, performance and cloud costs: the unglamorous work that keeps AI features standing.
How it works, step by step.
Scope the feature
We agree what the feature must do, how we'll measure it, and the smallest useful first version.
Ship a first version in two weeks
Working software in your stack, which your team reviews against the scope we agreed.
Make it production-ready
If the first phase satisfies you, you choose: get it ready for all your users, extend it, or stop. If it doesn't, you don't pay for it.
Recent AI product work
We're building an AI platform for Kliq Analytics in Canada that lets people query their data in plain English. For EquippedAI we built AI workflows into Minerva, their core platform, as part of a three-year product rebuild.
Read before you call.
Why your AI agent works in the demo and breaks in production
A demo proves the model can do a task once, on clean inputs, while someone watches. Production needs the system around it: saved state, checked outputs, focused context, logs, runs that survive restarts, and customer data kept apart. Design for these from day one; they are hard to add later.
How to check an AI agent's work before your customers do
An AI agent can sound right and still do the wrong thing, so 'it seems to work' is not a test. Check it in layers: hard checks on what it did, rubric scores for what it wrote, business-rule checks, and human review of samples and edge cases. Rerun the same cases after every change.
Why your RAG pipeline keeps disappointing
RAG works well for looking things up in stable documents. It disappoints when the answer depends on live state (this account's stage) or history (last week's call), which document search cannot reliably find. Use three layers: document search, direct lookups for current state, and a short memory summary.
Straight answers.
Something else on your mind? Email hello@coedify.com.
Do you work alongside our engineers?
Yes. We can own a feature end to end or work inside your team. Either way, your engineers see and review the work.
Which AI models do you use?
Whichever suits the job. We're not tied to one provider, and we'll explain the trade-offs in cost, speed and quality.
Can you work on an existing product?
Yes. Most of our work has been on products that already had users.
How does pricing work?
It depends on the nature of the work. After the first call we scope a two-week first phase and quote it before anything starts. If that phase doesn't satisfy you, you don't pay for it.
Who owns the code?
You do. The code and the IP we create for you belong to you, and we sign an NDA before you share anything sensitive.
Tell us what's slowing your team down.
You'll talk to Nadeem, our founder, and get a straight answer on what AI can and can't do for your team.
Md NadeemFounder & CEO, CoEdify
If the first two-week phase doesn't satisfy you, you don't pay for it.