[01.1]
Copilots & assistants
In-product assistants grounded in your docs, tickets and data with source citations.
Service [01]
AI Web Application Development
We design and engineer fast, secure web applications where AI is part of the product — not a bolted-on chatbot.
Problem and approach
The problem
Most teams prototype an AI feature in a weekend, then stall: hallucinations, latency, runaway token bills, no evals, and a UI that doesn't earn user trust..
Most teams prototype an AI feature in a weekend, then stall: hallucinations, latency, runaway token bills, no evals, and a UI that doesn't earn user trust.
Our approach
Production-ready from day one.
We ship the whole system — retrieval pipeline, guardrails, evals, cost controls and a UI built for streaming, citations and human override — so your AI feature survives real users.
Capabilities
What we deliver.
Modular capabilities we combine into one coherent system for your use case.
[01.2]
Semantic search (RAG)
Hybrid vector + keyword retrieval, re-ranking, chunking strategy and freshness sync.
[01.3]
Generative workflows
Draft, summarize, classify and extract — wired into forms, editors and dashboards.
[01.4]
Evals & guardrails
Regression test suites for prompts, PII redaction, jailbreak and toxicity filters.
[01.5]
Cost & latency control
Model routing, caching and streaming to keep p95 fast and spend predictable.
[01.6]
Scale & security
SSO, RBAC, audit logs, SOC 2-friendly infrastructure on AWS, GCP or Vercel.
Use cases
Where it pays off.
Examples of what this service can do for your business.
Example use case
B2B SaaS copilot
An in-app assistant that answers product questions and runs account actions from natural language.
Discuss a similar buildExample use case
Knowledge portal
A semantic search portal across 120k internal documents with permission-aware retrieval.
Discuss a similar buildExample use case
Generative CMS
An editor that drafts on-brand content, localizes it and routes it for approval.
Discuss a similar buildArchitecture
How it fits together.
A typical reference design — adapted to your cloud, data and security model.
How we build
From idea to production in 5 moves.
A fixed-scope, evidence-first process. You see working software every week — and real numbers before you commit to the full build.
1–2 weeks
Discover
We audit workflows, data and systems, then rank AI opportunities by ROI and feasibility.
2 weeks
Prototype
A working prototype on your real data, with evals that prove quality before we commit to a build.
6–12 weeks
Build
Weekly sprints shipping production code to your repos, with demos every Friday.
1–2 weeks
Deploy
Staged rollout with monitoring, guardrails, cost controls and team training.
Ongoing
Optimize
We measure outcomes, tune prompts and models, cut cost and expand to the next use case.
FAQ
Common questions.
Which models do you use?
We're model-agnostic: Claude, GPT, Gemini, Llama and Mistral. We benchmark on your data and often route between models for cost and quality.
Can you add AI to our existing app?
Yes. Most engagements extend an existing React, Next.js, Vue, Rails or Django codebase instead of rebuilding it.
How do you prevent hallucinations?
Grounded retrieval with citations, constrained outputs, automated eval suites and human-in-the-loop review for high-stakes actions.
Who owns the code?
You do. Everything we build lives in your repositories and cloud accounts from day one.
Explore
Other services.
AI Custom Application Development
Bespoke AI applications for web, mobile and desktop — built around your data, your workflows and your compliance requirements.
AI Agent Development
Autonomous and human-in-the-loop agents that plan, use tools and complete multi-step work across your systems — safely and observably.
Automation
Intelligent workflow automation that connects your apps, removes manual busywork and scales operations without scaling headcount.
[ Next step ]
Ready to scope your ai web apps project?
Book a 30-minute strategy call. We'll map your highest-ROI AI opportunities and tell you honestly what's worth building.