User engagement on our portal jumped 35% after IXT Minds shipped the video calling feature and frontend. They went beyond the original scope on things that mattered to us — without making it a negotiation.
Service — AI Solutions
Most AI demos die in the pilot. Ours go to production.
Chatbots, RAG, and agents built into the product you already have — by a senior team that owns the accuracy, the cost, and the upkeep long after the launch screenshot.
01 — What you get
Everything to take AI from a demo to a feature that earns its keep.
01
The right model, not the loudest
We match the model to the task — accuracy, latency, and cost weighed against each other. Claude, GPT, open weights, or a blend. You pay for the result, not the hype cycle.
02
Answers grounded in your data
RAG done properly — real chunking, embeddings, and reranking — so the model cites your truth instead of inventing its own. Fewer hallucinations. More trust.
03
Agents that fail safe
Tool-using agents that do real work, wrapped in evals, tracing, and fallbacks. When the edge case comes, it stops — instead of being confidently, expensively wrong.
04
Inside your product, not beside it
AI woven into the app and workflows you already have. Streaming responses, auth, and rate limits handled — so it feels like a feature, not a science project.
05
A bill you can predict
Caching, routing, and token budgets wired in from day one, with spend visible per feature. We tame the cost curve before usage scales it for you.
06
Yours to keep
The code, the prompts, the eval suite — all yours. No black-box platform, no per-seat AI tax, no ceiling the day you outgrow the vendor.
02 — How we work
Four steps. Measured, not guessed.
01
Frame
We pin down the task, the data, and what 'good' actually means — then write the eval set before a single model runs.
02
Prototype
A working slice in week one, tested against your real data. You judge quality with your own eyes, not a vendor's reel.
03
Harden
Guardrails, caching, tracing, cost controls — the unglamorous work that separates a demo from a feature. Then it ships into your product.
04
Improve
We stay on the model. Sharper prompts, a growing eval suite, falling cost per call — quality that climbs as usage does.
Built with
AnthropicOpenAIVercel AI SDKpgvectorRAGLangGraphNext.jsPostgresEvalsOpenTelemetry
03 — The choice
IXT Minds vs an agency vs a freelancer.
IXT Minds
Other agencies
Freelancer
Model choice
✓ Right model per task
Whatever's hyped
One API they know
Reliability
✓ Evals + tracing built in
Demo-grade
Hope it holds
Data grounding
✓ Proper RAG on your data
Generic prompts
Copy-paste prompts
Cost
✓ Budgeted and cached
Open-ended
Surprise bills
After launch
✓ Same team tunes it
Handover doc
Often unreachable
04 — Questions
Things people ask before starting.
It will, because we don't ship on vibes. Evals, guardrails, and tracing go in from the first commit, and we define what 'good' means before we build toward it. You launch a measured feature — not a coin flip wearing a confident tone.
No. We can use hosted models under strict data terms, run open-weight models inside your own infrastructure, or combine the two. The architecture bends to your privacy and compliance needs — never the other way around.
Caching, model routing, and token budgets from day one, plus dashboards that show spend per feature. We flatten the cost curve before scale does it for you — and you see every dollar, not a surprise at month-end.
Yes — most of our AI work lives inside apps that already exist. Streaming responses, auth, rate limits, and your current stack are all part of the build, so it lands as a native feature rather than a bolted-on widget.
The same senior team stays on it. Models drift, data changes, edge cases surface — so we keep tuning prompts, growing the eval suite, and driving cost down as real usage rolls in. Launch is where the work gets interesting, not where it ends.