AI Development & Integration
Devs & Logics adds production AI to your product, OpenAI, Anthropic, and Google models behind one routing layer, RAG over your documents, agents with tool calling, and eval suites plus cost controls so quality and spend stay predictable.
The problem we solve
Teams prototype ChatGPT wrappers that break in production: no evals, runaway API bills, hallucinations in customer-facing flows, and a hard-coded provider that turns every market shift into a rewrite. The model landscape now re-prices mid-quarter. You need engineering that treats AI as a product surface with limits, observability, and the freedom to switch.
What we deliver
We integrate AI into existing Next.js and Node apps for US SaaS teams, not standalone demos, so features ship inside your auth, billing, and compliance boundaries. And every feature ships with an eval suite, the same discipline behind our software testing service, so “the AI got worse” becomes a graph instead of a support ticket.
- Multi-model integrations: OpenAI, Anthropic, and Google behind one routing layer, switchable by config
- RAG and knowledge-base chat with grounded, cited answers
- AI agents with tool calling, guardrails, and human-approval steps where stakes demand them
- Eval suites as a deliverable: golden datasets, calibrated scoring, CI gates
- Cost engineering: semantic caching, batch jobs, tier routing, usage dashboards
- HIPAA-aware patterns where required
Our process
- 1
Use-case & risk review
Define what the model may and may not do, PII, HIPAA, or finance rules documented upfront, plus the routes: which tasks get a budget model, which earn the flagship, and what never goes to a model at all.
- 2
RAG or agent design
Embeddings, retrieval, prompts, and tool schemas, with the evaluation set built before launch rather than after the first incident. If the data an AI feature needs is locked inside legacy systems, this is where our enterprise IT services practice opens it up behind clean APIs first.
- 3
Integration & UI
Streaming chat, copilot panels, or background jobs inside your existing app architecture, your auth, your tenant model, your billing. The same patterns power AI-native product builds, from support copilots to AI-powered custom CRM systems with lead scoring and pipeline Q&A.
- 4
Cost & quality ops
Rate limits, caching, logging, and dashboards for token spend and failure modes, with eval gates wired into CI so prompt and model changes get scored before they merge, and a quarterly model review so provider price moves become opportunities instead of surprises.
Proof & outcomes
- Multi-provider LLM and vector database integrations on live SaaS products
- RAG chat and workflow automation shipped with evaluation before go-live
- Agent features with tool calling and guardrails running in production
- Token cost controls, caching, routing, budgets, for scaling user bases
Technologies
Related articles
Frequently asked questions
Can you add ChatGPT-style features to our existing SaaS?
Yes. We embed chat, copilots, or batch AI jobs into your current Next.js or Node app with your auth and tenant model, users never leave your product, and the features respect your existing billing and permission boundaries.
Which AI model should we build on?
In 2026, none exclusively. Providers re-price mid-quarter and leapfrog each other on capability, so we build behind a routing layer: budget models for classification and extraction, a workhorse for your main feature, and the flagship only where your own evals prove the gap is worth the price. Switching providers becomes a config change instead of a rewrite, and our published comparison of OpenAI, Anthropic, and Google keeps the current landscape mapped.
What is RAG and do you implement it?
Retrieval-Augmented Generation grounds answers in your documents or database instead of the model's memory. We build embedding pipelines, hybrid vector search, and cited responses for support bots, internal tools, and customer-facing knowledge features.
How do you test AI features before launch?
With eval suites, because conventional tests can't assert on probabilistic outputs: a versioned golden dataset of real cases, metrics per feature type, calibrated LLM-as-judge scoring, and CI gates that block a merge when answer quality regresses. It's a standard deliverable on every AI engagement, not an add-on.
How do you control AI API costs?
Four levers, applied together: semantic caching, cached input runs at roughly a tenth of standard rates, batch processing for non-interactive jobs at about half price, tier routing so simple tasks never hit flagship models, and per-user limits with usage dashboards. AI spend scales with revenue, not surprises.
Areas we serve
8 US markets. Explore local pages:
Discuss your AI Integration project
Share the feature you want and the data behind it. We'll propose the model routes, the eval plan, and a fixed-scope build.
Contact us →