- Billed hours
- 162
- Client rating on Upwork
- 5.0
AI writing assistant
AI features for SaaS
Highlight a passage and AI rewrites it in place. 162 hours, rated 5.0.
LLM integration services: AI features inside the SaaS you already run
In-place rewriting, chat, summaries, extraction and automation built into your Next.js, React or Python codebase, with a model per task and a bill you can forecast. A 162-hour in-editor writing assistant is the smallest example we have shipped.
Free 30-minute call. You leave with a written summary of the approach and a sensible first step. No deck, no pitch.
Discovery sprint from $2,500, builds from $6,000
Stack
- Claude API
- OpenAI
- Gemini
- Next.js
- React
- TypeScript
- Node.js
- NestJS
- Python/FastAPI
- Tailwind
Sound familiar?
AI is on the roadmap, but your team is at capacity
Your engineers are busy with the core product, and the AI feature slips to next quarter again.
A bolted-on chatbot doesn't help anyone
The value is in AI that works where users already are: the editor, the form, the record on screen.
You can't predict the model bill
Without caching, budgets and a model per task, usage costs climb with every new user.
What you get
- A written plan for the first AI feature, where it sits in your UI and how success is measured
- LLM features built into your existing UI and codebase, through pull requests your team reviews
- Streaming responses and in-place editing where they fit
- A model per task across Claude, OpenAI and Gemini
- Prompt caching, rate limits and usage budgets per account
- Tests, monitoring and handover to your team
How it works
01
Pick the first feature
We find the AI feature closest to something your users already do by hand, and write down how you will know it works.
02
Build it in your codebase
We follow your conventions and open pull requests your team reviews, one increment at a time.
03
Ship and measure
We release through your usual process, watch usage and cost, and plan the next feature from what the numbers say.
What LLM integration services include
LLM integration services add language-model features to software you already run: rewriting text in place, chat over your data, summaries, extraction from documents and automation of repetitive steps. At Tecifo that includes picking the first feature, building it in your codebase through pull requests your team reviews, and the caching, limits and monitoring that keep the bill predictable.
The smallest example we have shipped is a writing assistant inside a TipTap editor: highlight a passage, ask for a rewrite, and the OpenAI result replaces the selection in place. It took 162 hours and was rated 5.0. The largest is an enterprise knowledge platform where the AI layer grew over 1,145 hours. The method was the same for both: one feature closest to an existing workflow, shipped and measured, then the next.
Which AI feature to build first
Build the AI feature closest to something your users already do by hand, where success is easy to measure. A rewrite button in the editor they type in beats a chatbot in a new tab, because the user never leaves the task and you can count how often the suggestion is accepted.
The discovery sprint ends with that recommendation in writing: the feature, where it sits in your UI, which model handles it and why, how it will be measured, and what the second feature would be. You can hand that document to your own engineers if you would rather build it in-house.
How we keep the model bill predictable
A predictable model bill comes from four controls: prompt caching on the stable part of each prompt, a context budget per request so long inputs are trimmed rather than billed in full, a model chosen per task instead of the largest model for everything, and rate limits with usage budgets per account so one customer cannot run up everyone's cost.
We build with Claude, OpenAI and Gemini and choose per task: a small, fast model for classification and extraction, a larger one for long-form rewriting or reasoning over documents. Streaming goes in wherever a user waits on the output, so the feature feels responsive even when the model takes a few seconds.
How we work inside your codebase and alongside your engineers
We work in your repository, follow your conventions and open pull requests your engineers review, one increment at a time. Features ship behind your usual release process. Documentation and tests land with the code, so your team can maintain and extend the AI layer after we step back.
Your stack stays your stack. Next.js, React, TypeScript, Node.js, NestJS and Python/FastAPI are the ones we ship in daily, and if yours is something else we review it in the sprint before committing to a build.
What LLM integration costs and how long it takes
LLM integration at Tecifo starts with a discovery sprint from $2,500, which picks the first feature and writes the plan, and a fixed-scope build from $6,000. The in-editor writing assistant we shipped took 162 hours end to end; a first feature in an existing product typically takes a few weeks from plan to release.
If you want AI work to continue month after month, the ongoing team runs from $45 an hour with capacity agreed monthly and a written update per increment. The estimate moves with the number of features, how much of your data has to be prepared and whether the features write back into your product.
Chatbot in a new tab vs AI inside the workflow
Most SaaS teams start by asking for a chatbot. This is why we usually recommend the other column first.
Where it lives
- Chatbot in a new tab
- A separate chat panel
- AI inside the workflow
- In the editor, form or list the user already has open
How users find it
- Chatbot in a new tab
- They have to remember it exists
- AI inside the workflow
- It appears where the task is
Measuring success
- Chatbot in a new tab
- Message counts
- AI inside the workflow
- Suggestions accepted, time saved on a named task
Context the model gets
- Chatbot in a new tab
- Whatever the user pastes
- AI inside the workflow
- The selection, the record or the document on screen
Cost control
- Chatbot in a new tab
- Open-ended conversations
- AI inside the workflow
- Bounded inputs, a model per task, caching on the stable prefix
Example we shipped
- Chatbot in a new tab
- The default most demos start with
- AI inside the workflow
- Highlight to rewrite in a TipTap editor, 162 hours
Start inside the workflow. A chat surface can come later, once you know which questions users ask.
Questions, including cost and timeline
Tell us what you're building.
Free 30-minute call. You leave with a written summary of the approach and a sensible first step. Prefer writing? Send a brief and we reply by email with questions.
Discovery sprint from $2,500, builds from $6,000
Or email info@tecifo.com with a few times that suit you.Not ready to talk? Take the checklist.
Fifteen checks we run before real users and real data hit an AI product: prompt caching, context budgets, approval steps, row-level security, agent state, traces. We email you the link.