Software Development
// AI DEVELOPMENT
Production AI.Not demos.
We build, ship, and operate AI features inside real products. Agents, RAG, voice, vision, automation — engineered with evals, monitoring, and on-call from day one.
AI demos die before production.
// THE PROBLEM
Vapor demos
001
Looks great in a Loom.
Prototypes that ship as Twitter clips and never reach a real user. Polished walkthroughs, zero production hardening.
Cost surprises
002
Token bills nobody scoped.
First million tokens are cheap. The bill is not. Without ceilings, fallbacks, and observability, costs run ahead of revenue.
Hallucinations
003
No grounding, no evals.
Naked LLM calls with no retrieval, no citations, no test set. Trust collapses on the first confidently wrong answer.
No ops
004
Nothing to monitor.
No drift checks, no rollback path, no on-call playbook. When it breaks at 2am, your users find out before you do.
Engineered, not vibes.
// OUR APPROACH
Your product goes in. Production comes out. No demos, no drift, no surprises in between.
- 01 — SCOPED
We define done before we build.
Every feature ships against a measurable target — accuracy, latency, cost. If we can't measure it, we don't promise it.
- 02 — GROUNDED
Anchored to your data, not the model's guess.
Retrieval, citations and tool-calls wired into your systems. Hallucinations are a bug — caught by evals before users see them.
- 03 — OPERABLE
Built to run on a Tuesday at 3am.
Cost ceilings, drift alerts, fallback paths and on-call runbooks shipped with every project. Production-grade from day one.
Your product, AI-native.
// CAPABILITIES
[ 01 ]
Custom agents.
Multi-step, tool-using agents tuned to your domain. Built around your data, your tools, and your guardrails.
[ 02 ]
RAG over your data.
Indexed, cited, grounded answers from your private docs, live data, and APIs. Hybrid retrieval where it matters.
[ 03 ]
Voice & speech.
Realtime voice interfaces with low-latency TTS, ASR, and turn-taking. Built on the latest voice models.
[ 04 ]
Vision pipelines.
OCR, classification, detection, and visual reasoning over images and video. Production batch and realtime.
[ 05 ]
Automation flows.
AI-driven workflows that move data, fire actions, and stay observable end to end.
[ 06 ]
Evals & monitoring.
Test sets, regression gates, drift alerts, and rollback. Every deploy passes evals or it doesn't deploy.
Plan. Build. Operate.
// PROCESS
Three phases. No discovery purgatory. Every step has a deliverable.
STEP 01 · PLAN
A scope that survives contact with reality.
We start with the eval set, not the prompt. What does "correct" mean? What does "too expensive" mean? What's the failure mode we cannot ship with?
Once we agree on those, we pick the model, the architecture, and the cost envelope. Then we build.
STEP 02 · BUILD
Built like a product, not a notebook.
Prompts, tools, fallbacks, and telemetry all live in your repo. Every call is logged, every cost is tracked, every output is validated before it reaches a user.
By the time we hand it over, your team can review every line and own every decision. No black boxes, no vendor lock-in.
STEP 03 · OPERATE
Operated, not abandoned.
Evals gate every deploy. Costs and drift are monitored. Alerts route to a real person on call, not a dashboard nobody opens.
When something changes — a model deprecates, a customer reports weird output — there's a runbook waiting for the team picking it up.
FIRST FEATURE
3–6 wks
From kickoff to production.
GROUNDING
Built-in
RAG, citations, eval set.
COST CEILING
Locked
No surprise bills.
MONITORING
Day one
Evals, drift, on-call.
How Authect ships AI.
// THE AUTHECT BACKBONE
Same backbone for every project we deliver: scoped, cost-capped, evaluated, and operated. No exceptions, no surprises.
SCOPED
Eval criteria locked before kickoff
COST-CAPPED
Hard ceilings, alerts at 50/80/100%
GUARDRAILED
Inputs and outputs validated in code
authect://ai · production
$ authect ship █████╗ ██╗ ██╗ ████████╗ ██╗ ██╗ ███████╗ ██████╗ ████████╗ ██╔══██╗ ██║ ██║ ╚══██╔══╝ ██║ ██║ ██╔════╝ ██╔════╝ ╚══██╔══╝ ███████║ ██║ ██║ ██║ ███████║ █████╗ ██║ ██║ ██╔══██║ ██║ ██║ ██║ ██╔══██║ ██╔══╝ ██║ ██║ ██║ ██║ ╚██████╔╝ ██║ ██║ ██║ ███████╗ ╚██████╗ ██║ ╚═╝ ╚═╝ ╚═════╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝ ╚═════╝ ╚═╝ > Loading project: client-app · production > Running eval suite... 142 tests · 98% pass > Cost ceiling: $200/day · alerts ON > Guardrails: inputs + outputs validated ✓ Ready. Telemetry: minimal · Evals: green $
EVALUATED
Tests gate every deploy
PORTABLE
Your code, your repo, your IP
SUPPORTED
On-call runbook on day one
SCOPED
Eval criteria locked before kickoff
COST-CAPPED
Hard ceilings, alerts at 50/80/100%
GUARDRAILED
Inputs and outputs validated in code
EVALUATED
Tests gate every deploy
PORTABLE
Your code, your repo, your IP
SUPPORTED
On-call runbook on day one
Frequently asked questions.
WHAT MODELS DO YOU USE?
Whatever fits the job: GPT, Claude, Gemini, Llama, open-weight via vLLM. We pick per-feature, not per-religion. Most products end up multi-model.
WHO OWNS THE IP?
You do. All code, prompts, eval sets, and any weights we fine-tune are yours. We don't keep a copy of your data after handover.
HOW DO YOU HANDLE PRIVATE DATA?
Tenanted infrastructure, no training on your data, encryption at rest and in transit. We can deploy to your own cloud if compliance requires it.
DO YOU FINE-TUNE?
When evals show it will move the metric, yes. Otherwise we go RAG-first; tuning is a last resort, not a default. Cheap, fast, evaluated, in that order.
WHAT DOES PRODUCTION-READY MEAN?
Monitoring, evals gating deploys, cost ceilings, fallbacks, and an on-call runbook. Every feature ships with all five — no exceptions.
HOW DO YOU SCOPE COST?
A per-feature cost ceiling is agreed before kickoff. Hard caps in code. Alerts at 50%, 80%, 100%. Automatic fallbacks to cheaper models on overage.
Start a project.
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info@authect.com
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