Anthropic Partner Network · Builders, not advisors

We embed with your team
and ship AI into the workflows
that run your business.

Quoting, scheduling, billing, delivery — production systems in your stack. Measured in cycle time, not slideware.

Senior engineers · you own the code · ship in weeks 5.0/5 on Clutch

Trusted by teams that run on us
Seegrid Cars.com AblePay HBnext Onward Robotics Sommer Hope Centers AccuTrade Arcadia

What we do

Three practices. One team.

Custom Software

ERP, platforms, and the integrations between them. Our largest practice.

Explore custom software →

Cloud & DevOps

AWS, Terraform, Kubernetes. The foundation the other two stand on.

Explore cloud & DevOps →

AI Quoting Accelerator

How long does a quote take you?

One quoting workflow, end to end: the system reads the RFQ, drafts the quote with your pricing logic, and routes it to your estimator for approval — then writes back to your ERP.

96%
reduction in quoting search time
CADDI, 2026
30–40%
shorter quote cycle times
Deloitte
8 weeks
from kickoff to production pilot
Our offer terms

Those numbers come from rigid products and Fortune 500 programs. We build the same capability around your pricing logic, in your systems, at mid-market price.

Case studies

Real numbers from real engagements.

How we work

Embed. Ship. Measure.

01

Embed

Senior engineers join your team — your repos, your stack, your security model.

02

Ship

Production in weeks, not quarters. Human-in-the-loop until the accuracy data says otherwise.

03

Measure

Success is a number you already track — cycle time, win rate, hours saved — agreed up front.

Our own first case study

We run on what we sell.

Our sales pipeline runs on an AI intelligence system we built. Our inbox, Slack, and ticket triage are agentic. When we propose AI in your operations, we're describing how we already run ours.

quoting/rfq-handler.ts
Embed → Ship
− before
01import { parseRFQ } from './parser'
02import { ERP } from './erp-client'
03
04export async function handleRFQ(
05 rfq: RFQDocument
06) {
07 const parsed = await parseRFQ(rfq)
08 // estimator manually builds quote
09 const quote = await
10 manualEstimate(parsed)
11 // avg 4.5 hours per quote
12
13 return ERP.createQuote({
14 lineItems: parsed.items,
15 pricing: quote.pricing,
16 status: 'draft'
17 })
18}
+ after
01import { parseRFQ } from './parser'
02import { ERP } from './erp-client'
03import { AIQuoter } from '@nextlink/ai'
04export async function handleRFQ(
05 rfq: RFQDocument
06) {
07 const parsed = await parseRFQ(rfq)
08 // AI drafts quote, human approves
09 const quote = await
10 AIQuoter.draft(parsed, pricingRules)
11 // avg 18 min — 94% confidence
12
13 return ERP.createQuote({
14 lineItems: parsed.items,
15 pricing: quote.pricing,
16 aiConfidence: quote.confidence,
17 status: 'pending_review'
18 })
19}

Tell us which workflow is the bottleneck.

We'll tell you if AI can fix it — and what it can't do yet.

Tweaks

Case study image