Fewer steps for your users
Inline suggestions and smart defaults take work out of a flow people already understand, instead of adding a new surface they have to learn.
Service
AI Enablement: LLM Integration & In-Product AI
Your existing product, with AI where it actually helps.
Adding AI to a product that already works is a different job from building an AI product. The risk is not that it fails to impress: it is that it makes something reliable feel unpredictable, and users lose trust in the parts that were fine.
We add it carefully. AI enablement at Zefract means choosing the right model for each job, grounding it in your data, and embedding it where it removes a step people currently resent, with evaluation and cost control so the feature stays good and affordable after launch.
LLM Integration
Choosing the right model per task rather than defaulting to the largest, and connecting it properly. A cheap fast model handles most jobs; the expensive one is reserved for the ones that genuinely need it.
Orchestration across providers with fallback handling means an outage or a price change is a configuration decision rather than an incident and a rebuild.
Includes- Model selection
- API integration
- Provider orchestration
- Fallback handling
Data Grounding
Connecting the model to your reality. Data connectors and retrieval so answers come from your systems, and context engineering so the model is given what it needs rather than everything available.
Guardrails define what it may discuss and what it must refuse. That boundary is a product decision, and writing it down is what makes the feature reviewable.
Includes- Data connectors
- Context engineering
- Retrieval
- Guardrails
In Product AI
Intelligence embedded where the work already happens. Inline suggestions and smart defaults take a step out of a flow people already understand rather than adding a new one to learn.
The features that survive are usually the least visible. A field that filled itself in correctly gets used every day; a chat panel gets opened twice.
Includes- AI feature embedding
- Inline suggestions
- Smart defaults
Prompt Engineering
Prompts treated as code: designed, versioned and evaluated against a real test set, so a change can be shown to be an improvement rather than assumed to be one.
Evaluation is what makes the feature maintainable. Without it, nobody can safely touch a prompt that is working, and quality drifts silently as models are updated underneath you.
Includes- Prompt design
- Evaluation
- Versioning
- Cost control
Why it matters
The best AI feature is one users never have to think about.
The AI features people actually keep using are rarely the ones with a chat box. They are the smart default that was already correct, the suggestion that appeared at the moment it was useful, the field that filled itself in. Visible AI asks for effort and trust; invisible AI simply removes work.
Here’s what doing it well returns:
Outputs you can rely on
Prompts designed, versioned and evaluated against real cases, so quality is measured rather than assumed and a change can be shown to be an improvement.
Costs that stay predictable
Model selection per task, caching, and orchestration across providers with fallback handling. Token spend is a design decision rather than a surprise on the invoice.
Not locked to one vendor
An abstraction over providers means a price change, an outage or a better model next quarter is a configuration decision rather than a rebuild.
It degrades gracefully
Fallback handling for when a provider is slow or down, so an AI feature having a bad day does not take a working product with it.
Trust in the rest of the product
AI added where it removes a step, not everywhere it could go. The fastest way to make reliable software feel unpredictable is to sprinkle probabilistic output through the parts that were fine.
AI is worth adding where it removes work someone is currently doing by hand. Everywhere else it is a feature that demos well, gets used twice, and quietly costs you money every month.
Why AI Enablement with Zefract
One team, one goal, nothing lost in the handoff.
The people adding AI to your product understand the product, because product engineering is the same bench. The feature fits the thing it is going into rather than sitting beside it, and we will say plainly when a use case is not worth the tokens.
Want AI in your product without the risk?
Start with a feasibility callFAQ
Frequently asked questions
Scale and starting point. Enablement adds AI to a product that already exists and works, usually as features inside flows people already use. Applied AI builds something new, typically agents or assistants that do a job end to end. Most companies want enablement first.
Different ones for different tasks, which is usually the cheapest correct answer. A small fast model handles most jobs at a fraction of the cost, with the expensive one reserved for work that genuinely needs it. We build against an abstraction so the choice can change without a rebuild.
By treating token spend as a design decision. Model selection per task, caching for repeated work, prompts kept tight, and limits set deliberately. We instrument usage from the start so cost is visible per feature rather than arriving as one unexplained line on a monthly invoice.
The feature degrades rather than the product failing. Orchestration across providers with fallback handling means a slow or unavailable model routes elsewhere or fails gracefully into the non-AI path. An outage at one vendor should not take a working product with it.
Evaluation against a real test set, run whenever a prompt or model changes. Without it nobody can safely touch something that is working, and quality drifts silently as providers update models underneath you. Prompts are versioned and treated as code for the same reason.
Next step
Ready when you have a brief.Or half of one.
Send whatever you have. You get scope, a timeline and a number back within three working days.
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