Integrate an LLM into an existing SaaS product
Add generation, summarization, extraction, or a model-assisted workflow to a product without treating the model call as the whole feature.
LLM applications and integrations
LLM application development for production SaaS.
Techtribe provides LLM application development and AI integration services for SaaS products, from a focused feature to a multi-model platform. We connect model APIs to real user workflows, then add the structured outputs, evaluations, fallbacks, and monitoring the agreed production scope needs.
One Islamabad-based team · direct founder access · code in your repository
Where it fits
What this service covers.
Build a multi-model AI application
Connect model providers behind a product interface, with routing and fallbacks chosen around task quality, latency, and cost requirements.
Generate structured outputs
Turn documents, conversations, or user inputs into validated fields and actions that fit the surrounding product workflow.
Create AI-powered content tools
Build product features that combine language, image, audio, or video model APIs inside one coherent application experience.
Engineering approach
Designed around the product and its constraints.
We choose the implementation after understanding the user flow, existing systems, and operating requirements. The exact stack and boundaries are agreed during scoping.
Model and API integration
Connect OpenAI, Anthropic, Google, or other agreed model providers to application services and user-facing product flows.
Structured workflows and tool use
Use typed inputs and outputs, tool contracts, background jobs, and streaming interfaces where the product needs them.
Quality and production operations
Build evaluation sets, regression checks, provider fallbacks, and monitoring for quality, latency, and cost into the release scope.
Technology
A stack selected for the system.
Techtribe’s published work includes model integrations across OpenAI, Anthropic, Google Generative AI, and other providers. Application stacks have included TypeScript, Next.js, Node.js, Python, and FastAPI; the final selection depends on your product and infrastructure.
TypeScript, Next.js
Web application surfaces and product workflows for LLM features.
Node.js, Express
Application APIs, provider integrations, and asynchronous product services.
Python, FastAPI
AI services and integrations where Python fits the existing system.
OpenAI, Anthropic, Google
Model providers used in published work; selected and evaluated for the task.
MongoDB, Redis, AWS
Data, caching, and cloud components represented in published product work.
Choose the right scope
Start with the smallest scope that fits.
Package fit depends on the workflow, integrations, and production requirements. We confirm the scope, price, and ship date before work starts.
2 weeks
Basic MVP
$2,500fixed
Founders who need something real to show users or investors, fast.
One core product flow with a single AI feature to validate a focused LLM use case.
- One core user flow, working end to end
- One AI feature: agent, chat over your data, extraction or automation
- Clean, responsive UI on our design system
- Deployed on a live URL you can share
- Source code in your repo from day one
- 30 days of free support
Not included: Production hardening (evals, monitoring, fallbacks), custom brand design, more than one integration. Everything built here carries forward into Production MVP.
4–6 weeks
Production MVP
$7,000fixed
Teams launching an AI-first product, or a serious AI module, that real customers will depend on.
A customer-facing LLM feature or AI product with design, integrations, deployment, evaluation, and monitoring.
- UX and UI design in your brand, before we write code
- Production architecture: deterministic flow control, typed tool contracts, model routing with fallbacks
- Web app and backend deployed on your cloud, in your repo
- Auth, billing hooks and admin basics
- Up to 3 integrations into your existing systems
- Eval set and regression tests
- Cost, latency and quality monitoring
- Weekly live demo
- 30 days of free support
Rolling · 30 days' notice
Dedicated Team
$8,000per month
Teams that shipped and want to keep building, or companies that need a senior AI pod without hiring one.
Ongoing AI application development across product features, model integrations, and operational improvements.
- 2 engineers (1 senior, 1 mid), full-time on your product
- 1 dedicated project manager: your single point of contact
- Founder oversight on architecture and quality
- Daily standup, weekly demo, shared Slack
- 4+ hours overlap with US Eastern, full day with Europe
- Code in your repo, infra in your cloud, nothing to migrate
- Swap or add engineers with 2 weeks' notice
- 30 days of free support if you pause or wrap up
Relevant work
See deal.ai in practice.
On deal.ai, Techtribe’s founders helped ship an AI App Wizard, Flux LoRA fine-tuning, and real-time generation infrastructure as the platform expanded to more than 75 tools. The Arttribe case study shows a separate multi-model creative product built across image, video, music, and voice.
How it works
A clear path from scope to shipped software.
01 · Scope the user workflow
Define the feature, model inputs and outputs, product constraints, data boundaries, and success criteria.
02 · Design the integration
Choose the application flow, provider strategy, data handling, evaluation approach, and failure behavior.
03 · Build and evaluate
Implement the feature, review it against representative cases, and share a working demo each week.
04 · Ship and support
Deploy to the agreed environment, hand over the code, and support the shipped scope for 30 days.
FAQ
LLM application development questions.
What is LLM application development?
It is the product engineering around a language model: connecting model APIs to a user workflow, handling inputs and outputs, integrating tools or data, and evaluating the resulting feature.
Looking for an OpenAI integration company?
Techtribe can integrate OpenAI or another provider into an existing application. We scope the API boundary, user experience, data handling, and how the feature will be evaluated.
Do you work with more than one model provider?
Yes. Published work includes OpenAI, Anthropic, Google, and other model integrations. We choose providers based on the task and can design routing or fallbacks where the scope needs them.
When does an application need RAG?
RAG is useful when a feature needs to retrieve relevant information from selected documents or company data. See our RAG development page for the retrieval and access-control work involved.
How do you evaluate an LLM feature?
We agree on representative inputs and expected behavior, then use them to check outputs and regression cases. Production scopes can add monitoring for quality, latency, and cost.
Which package fits an LLM application?
Basic MVP can validate one AI feature, Production MVP suits a more complete launch with integrations and evaluation, and Dedicated Team supports ongoing product development. We confirm the package against your scope.
Book a free call
Adding an LLM feature to your product?
30-minute free call. Tell us what the feature needs to do, what systems it must connect to, and how users will rely on it. We’ll shape a clear technical scope and package fit.
- You talk to a founder, not a salesperson.
- You leave with a recommended package and a rough plan.
- Prefer email? howdy@techtribedev.com