milebits
Book →
Currently accepting new engagements

Production AI, insideyour infrastructure.

Founder-led. Senior engineers with a decade each in production systems. We build inside your infrastructure, ship to production, and hand off the keys. Default engagement is scoped to reach production by day 30. Honest timeline on the first call if yours needs more.

Book a 20-min callHow we ship in 30 days
Day 30
Typical production milestone, in writing on day one
8 mo
Industry average, AI prototype to production
Week 1
Working prototype on your calendar
Your repo
All code in your branch, from day one
Ownership

Revoke our access on a Monday morning.The system keeps running.

This is the only positioning we genuinely care about. Every architecture decision, every observability choice, every infra setup is made so the work continues after we leave. Not as an exit clause buried in the MSA. As the default state of the engagement.

01

Code lives in your repo.

From day one. Every commit, every pull request, in your branch. There is no milebits monorepo. There is no proprietary platform. There is only your repository, with our work in your history.

02

Infra runs in your cloud account.

Your AWS, your GCP, your Azure. We hold IAM roles you control and can revoke. We never operate shared infrastructure on your behalf. You are not migrating off us in two years because there is nothing to migrate.

03

Knowledge transfers in writing.

Runbooks in your wiki. Architecture diagrams in your repo, kept current. A recorded walkthrough with whoever takes over. Two-week overlap on handoff. The exit is designed before the engagement starts, not at the end.

If a vendor cannot describe what happens to your business when they go away, they are not building an infrastructure relationship with you. They are renting you a dependency.

Why we exist

The gap between “we should build this” and “this is in production” has gotten worse, not better.

The models keep improving. The path to production hasn't. Teams spend the better part of a year on a pilot, watch it stall in security review, and the budget evaporates with nothing in front of users.

95%

of enterprise GenAI pilots deliver zero measurable P&L impact.

MIT NANDA, State of AI in Business, 2025

42%

of companies abandoned most of their AI initiatives in 2025. A year earlier it was 17%.

S&P Global, 1,000+ enterprises, 2025

8 mo

on average to get an AI prototype into production. Barely half ever make it.

Gartner survey, 2024

40%+

of agentic AI projects will be canceled by end of 2027, on Gartner's current projection.

Gartner, June 2025

Why it stays broken

Three forces benefit from the status quo. None of them are your fault.

Consulting-led AI transformation.

Multi-quarter engagements that produce maturity assessments, governance frameworks, and slide decks. The thing that was supposed to be in production by Q3 is now a 47-page strategy document.

Platform vendor lock-in.

“AI platforms” that own the data flow, the evaluation surface, the deployment target. Six months in, you can't migrate off them without rebuilding from scratch.

Pilot purgatory incentives.

Internal innovation teams measured on launches, not production. The pilot gets a demo at the all-hands. The handoff to a team that owns it in prod never happens. The pilot dies in Q4.

It isn’t a skill problem

Why this stalls in-house, even with the right team.

Most CTOs we talk to have engineers who could absolutely build this. The reason it isn’t shipping isn’t talent. It’s four system-level frictions that no amount of seniority can engineer around alone.

  1. 01

    Coordination tax

    Production AI isn't an engineering project. It's a coordination project across data, platform, security, legal, product, and the team that asked for it. Your senior engineers end up spending 60% of the week in meetings to keep eight people aligned on a single deployment.

  2. 02

    Incentive misalignment

    Your research team is measured on demos and novelty. Your platform team is measured on reliability and on-call. Production AI sits in the seam where neither incentive points. Six months later the pilot is still a pilot, and nobody whose bonus depended on shipping it.

  3. 03

    Infra fragmentation

    Three teams independently pick three models, three vector stores, three observability stacks, three eval frameworks. Now you're running an internal AI platform instead of shipping AI features. The platform takes a sixth headcount you don't have.

  4. 04

    Slow decision loops

    Model selection goes through a committee that meets every other Wednesday. Security review is four weeks. Procurement is three. Vendor onboarding is two. Your engineers are good. The system around them adds nine weeks to every decision.

A pod walks past all four of these because we’re not embedded in your incentive structure or your platform politics. We work to one written deliverable, on your sprint cadence, until the thing is in production. Then we leave the runbook and step out.

The real comparison

Most teams aren’t deciding between us and another agency. They’re deciding whether to hire two engineers.

Hiring is usually the right answer eventually. It’s rarely the right answer now. Here’s the honest math on the next twelve months.

Axis
Hire 2 senior engineers
Engage a milebits Pod

Time to first commit in your repo

8–14 weeks (post-offer, post-notice, post-onboarding)

Day 1

Time to first production deploy

4–6 months (ramp, context, first non-trivial PR)

Week 3

Year-one all-in cost for 2 senior engineers

≈ $480K (salary + equity + benefits + tooling + recruiter fees)

≈ $385K for a Pod retained 10 months

Risk of a mishire

Real, slow to admit, and months more to undo

Pause or end on 30 days’ notice, no severance

AI-specific production experience

Scarce. You’re bidding against every funded startup and big-tech lab for the same shortlist.

Senior engineers with a decade each in production systems

When the work is done

Awkward. You let people go or invent a new charter.

We leave the runbook and the code. You keep going.

When hiring is the right call: if you’ve already shipped a v1 to production and you need someone to own the system for the next three years. We’ll tell you that on the first call. Our engagement is designed to hand the keys to your team and step out, not to become a recurring line on your P&L.

What we ship

Six things we're good at. We'll tell you if your problem isn't one of them.

Each item below is something our pods deliver inside a Sprint or Pod engagement. Pricing lives in one place, further down. You pick the tier, we configure the work.

01Sprint or Pod

AI agents that actually ship

Not a demo. Not a pilot. A working agent in your stack, handling real load by week four.

  • Frontier model of your choice routed through your data. Snowflake, Postgres, Notion, whatever you have.
  • Failure modes we obsess over: hallucinations, drift, runaway costs, prompt injection.
  • Eval harness from day one. Accuracy is a number you set, not a vibe we report.
  • Observability stack in your account. Every prompt, completion, and cost line queryable.
What success looks like
Tickets deflectedTime-to-resolution droppedCost-per-interaction tracked
02Sprint or Pod

Voice AI for the phones nobody picks up

Inbound, outbound, after-hours, overflow. Sounds human, books the meeting, doesn't quit at 5pm.

  • Built on Retell, Vapi, or your provider of choice. We don't sell you a stack.
  • HIPAA-aware setups for healthcare front offices, BAA-ready.
  • Routes to a human the second confidence drops below your threshold.
  • Per-minute economics you can actually live with, not the marketing number.
What success looks like
After-hours capture rateMeetings booked per dollarHuman-handoff rate
03Pod

Copilots trained on what your team knows

RAG done by people who've watched RAG go sideways. Real retrieval, real evals, real answers.

  • Your docs, tickets, wiki, Slack history. Chunked and indexed properly, not naively.
  • Permissions respected. If a user can't see the source, the copilot won't quote it.
  • Eval harness from day one. Accuracy measured before adoption is celebrated.
  • Plugs into Slack, Teams, your app, or a standalone surface you control.
What success looks like
Tier-2 deflectionTime-to-answerCitation accuracy
04Pod or Fleet

Embedded engineering pods

Senior engineers embedded in your Slack. Daily standups. Code in your repo from day one. Technical lead, delivery, on-call covered.

  • A decade each, hands-on. No offshoring, no junior bench, no PMs in your way.
  • We work to your sprint cadence, your CI, your branching model. Not ours.
  • Hiring plan, infra plan, due-diligence prep, all included if you need them.
  • Pause or end on 30 days' notice. We stay as long as we're earning our keep.
What success looks like
Roadmap velocityTime-to-hire bridgedAudit-readiness reached
05Sprint or Pod

SOC 2 without the theater

We don't sell you Vanta. We sell you a SOC 2 Type II audit you'll actually pass.

  • Vanta or Drata partner pricing passed straight to you (20–40% off list).
  • Policies written for your stack, not a template from 2019.
  • Auditor introductions, scoping calls, and evidence collection handled.
  • Typical audit-readiness window is 6 to 10 weeks, not six months.
What success looks like
Pipeline unblockedTime-to-auditExceptions on first pass
06Sprint or Pod

Internal tools & automation

The stuff your ops team is doing in 14 spreadsheets and a Slack channel right now.

  • Retool, n8n, Make, or hand-rolled Next.js. Whatever fits the actual job.
  • Self-hosted by default. You own it. We don't bill per workflow or per seat.
  • Integrations with HubSpot, Stripe, Salesforce, NetSuite, and the long tail.
  • Built once, maintained on a small retainer if you want it maintained.
What success looks like
Hours reclaimed/weekError rate droppedPer-task SaaS fees eliminated
Who’s building this

Founder-led. Senior end to end.

milebits is built by senior operators with a decade in production AI, infrastructure, and platform engineering. No agency layer. No junior bench. No PMs filtering Slack.

We started milebits because the AI implementation gap is an engineering problem, not a consulting problem. The firms trying to close it with frameworks and maturity assessments keep proving the point. We’d rather be the team that ships the thing than the team that writes the deck about shipping the thing.

You work with the people who design the architecture. The engineers who scope your engagement write the code, take the on-call rotation, and answer when something breaks. There is no associate to escalate to and no offshore handoff to wait on.

The team scales when the work demands it. We add senior engineers we’ve shipped with before, never a bench we need to keep busy. Seniority is the constant, not the headcount.

That’s also the reason for the pricing. Direct access to senior operators is not free, and production AI is not appropriate work to delegate to anyone less.

How we ship
How we work

Working software in week one. Production by week four. Or we don't bill you for week five.

We ship on a cadence that's uncommon for the category. We can do it because the engineers on your pod bring a decade each of prior production experience with the stack, and because the scope of week one is written down before we touch a keyboard.

Week 01/5

The 20-minute call

You tell us what's broken. We tell you whether we should be the ones fixing it. If we're not, we'll usually know somebody who is.

You get

Honest yes or no, in writing, before you leave the call.

Week 12/5

Working prototype

Senior engineers in your Slack on day one. By Friday there's a thing you can click, prod-quality enough to show your team.

You get

Loom walkthrough, sandbox URL, weekly demo on the calendar.

Week 23/5

In your repo

Code lands in your GitHub. PRs reviewed by your team. Infra in your cloud account, your IAM, your observability stack. Never ours.

You get

PRs merged, env vars handed off, runbook drafted.

Week 34/5

Behind a flag

Deployed to production. Gated by your feature flag tool of choice. We dogfood for 48 hours, then your team picks the rollout pace.

You get

Staged rollout plan, observability dashboards, on-call rotation.

Week 4+5/5

Shipping for real

Full traffic. Iteration begins. Weekly demos continue. You can pause us at 30 days' notice from this point on. No annual lock-in, ever.

You get

Real users, real cost line items, real numbers in your dashboard.

How we’ll keep this honest

Promises are cheap. Mechanisms aren’t.

Every claim on this page is enforced by something operational. If we can’t name the mechanism, we don’t make the claim. The four questions every serious CTO asks before signing, with the mechanism for each one underneath.

01Mechanism

How do I know you’re actually shipping, not building toward a demo?

PRs land in your repo daily. Demos happen every Friday.

  • ▸Pull requests land with a description, tests, and a 60-second Loom. No “trust us” merges.
  • ▸A standing 30-minute demo on your calendar every Friday, your team picks who attends
  • ▸We work on your branch protection rules. If you require two reviewers, we require two reviewers.
  • ▸Revoke our repo access on a Monday morning and the system you have keeps running
02Mechanism

How do you prevent the AI from doing something embarrassing?

An eval set you sign off on, before anything reaches your users.

  • ▸We build the eval set with your subject-matter experts in week one. Your prompts, your expected outputs.
  • ▸Accuracy threshold is a number we agree on before launch. We don’t flip the flag until we hit it.
  • ▸AI calls route through a confidence threshold by default. Under it, we hand off to a human, log the case, and feed it back into evals.
  • ▸Hallucinations are tracked in your dashboard as a metric, not discovered in a post-mortem
03Mechanism

How do you keep the model bill from blowing up at 2am?

Per-tenant token budgets and a cost alarm wired to your Slack.

  • ▸Every integration ships with a per-tenant token budget. Runaway loops hit the budget, not your card.
  • ▸Cost alerts land in your Slack at 80% of the budget, well before anyone notices
  • ▸Model selection is layered. Cheap model for cheap tasks, premium model only when accuracy demands it.
  • ▸Caching, batching, and prompt compression applied by default, not as an optimization later
04Mechanism

What happens if we want you gone in 90 days?

You get a runbook, a recorded handover, and an empty calendar invite for us.

  • ▸At engagement end: a written runbook for every system we built, kept in your wiki
  • ▸A 60-minute recorded walkthrough of each system with your in-house engineer
  • ▸A two-week overlap with whoever’s taking over (your hire, your existing team, another vendor)
  • ▸Our repo access, infra access, and Slack invites revoke on day one of the wind-down
Constraints

Where 30 days slips.

Years inside engineering teams taught us where these projects stall. None of the six below are our process failures. They’re operational realities we either prevent in advance, name out loud on day two, or absorb together. A vendor that won’t name these in writing is the wrong vendor.

01

The data isn't where you think it is.

The customer-support knowledge for the copilot turns out to live in seven Salesforce custom objects, two Notion workspaces nobody owns, and a CSV someone exports manually on Fridays. Week one slips while we map the actual source of truth.

Our move

Stop, write a two-page data inventory, get sign-off from whoever owns each source. Resume on a slightly later baseline.

What you bring

A senior person who can adjudicate 'which source wins' inside a 48-hour window.

02

InfoSec adds two to three weeks we can't influence.

Your security team has its own queue. We're vendor forty-seven this quarter. They will not be rushed, and they shouldn't be. The clock starts when their ticket starts.

Our move

Pre-fill the questionnaire on day zero, line up BAA, DPA, and vendor-risk forms before kickoff. Beyond that, we wait alongside you.

What you bring

A named InfoSec contact and willingness to escalate when the queue stalls past the timeline you committed internally.

03

The executive sponsor leaves.

The person who scoped the engagement with us moves on. Their successor wants to relitigate the deliverable. Both outcomes are possible: a cleaner scope after the re-kickoff, or a graceful pause. We can't predict which on day one.

Our move

Stop spending against the old scope. Run a one-meeting re-kickoff with the new owner. Get the new deliverable confirmed in writing before we touch the keyboard again.

What you bring

Continuity of intent, even when the person changes. Or an honest 'pause us' conversation.

04

The team that asked for this gets cold feet at staging.

We ship to staging on time. The team that needed it now wants three more weeks of QA, or a feature added before launch, or a tabletop with leadership. The instinct is real; it isn't always risk.

Our move

Distinguish 'real risk found' from 'fear of going live.' Real risk gets fixed. Fear gets a conversation about what would actually unlock the deploy.

What you bring

A decision-maker who can say 'ship it' when the data says ship it.

05

A dependency we don't control is late.

Your vendor's API key takes twelve days. Your billing system's read access takes nine. Your CRM admin is on vacation through the rollout week. We can't make these go faster than your vendors will.

Our move

Sequence the work to keep moving on what we can build. Tell you on day two, not day twenty. Re-baseline the timeline before it embarrasses anyone.

What you bring

A clear path to escalate the dependency, and acceptance that we can't engineer around your vendors.

06

We are wrong about the approach.

Week-two demo reveals the architecture won't scale, or the model won't behave inside your data, or the integration is too brittle for the SLA you need. We have to backtrack.

Our move

Tell you the truth on Friday. Show the work that led to the conclusion. Propose the corrected path with new scope and timing. No invoice hiding inside the next sprint.

What you bring

Tolerance for 'we got this wrong' as a one-day reset, not a relationship-ending event.

If any of these turn into outright blockers mid-engagement, the right call is to pause. We’ll say so before the next invoice. Pausing honestly is part of the operating model; quietly running the meter past the point of value is the opposite of how this practice is being built.

How we’d approach this

Three scenarios. Walked through the way we’d actually plan them.

Not case studies. Approach documents, written so you can see how we reason about a system before signing anything. Click any scenario to read how we’d architect it.

SCENARIO / 01Approach

Support deflection agent at a mid-market SaaS

A 200-person B2B SaaS drowning in tier-1 tickets. Looking at an agent to deflect.

The problem

Volume is real, ticket history is large, knowledge base is decent but stale. The team has been pitched LangChain + a vector DB + a custom UI by another vendor. They are not sure what they actually need.

What we’d refuse to do

We would not build it on top of an AI platform you would be locked into. We would not auto-respond to billing or legal categories without an explicit human-in-the-loop. We would not promise human-quality responses across the board.

Week 1: baseline

Read-only access to ticket history and the KB. Build the eval set from real tickets the support team flags, not synthetic ones. Run a vanilla model with retrieval and measure deflection. That number is the floor. Everything we build has to clear it.

Architecture

Postgres with pgvector for retrieval, one typed function calling Claude with structured output, our own thin orchestration. No framework-of-the-month. Boring on purpose, because boring stays in production.

Failure modes we plan for

Hallucinations caught by self-verification on factual claims. PII leak caught by an output filter that runs before the response is returned. Cost runaway caught by a per-conversation token budget tied to a Slack alert.

Production readiness

Feature flag gated to 5 percent of inbound. Helicone wired up to your account, not ours. Weekly review of low-confidence responses with the human team. Roll-up by support category.

SCENARIO / 02Approach

Voice AI for a multi-location services business

A home services company across 12 locations, missing roughly 30 percent of after-hours calls.

The problem

They want to deploy voice AI for inbound. Their assumption is that it should handle everything. The reality, once we pull recordings, is usually that 60 percent is scheduling, 25 percent is pricing, 15 percent is complaints that need a human. Voice AI fits two of those well and one of them terribly.

What we’d refuse to do

We would not deploy voice AI on calls where the wrong answer creates legal liability. We would not tell you the AI handles it all when it handles 75 percent well. We would not bill per minute on top of the voice provider's bill.

Week 1: triage

Pull a week of call recordings with consent. Manually categorise. Show the team the actual distribution and what voice AI would credibly handle. This conversation often reshapes the scope before we touch code.

Architecture

Retell or Vapi for the voice surface, depending on their HIPAA posture. A Claude function-calling endpoint for scheduling logic. Their existing CRM stays the system of record. We do not add a new database.

Latency budget

600ms median round-trip target. If we are over 800ms median by week three, we revise the architecture instead of hoping. The voice surface is the unforgiving part of the system.

Handoff design

Confidence thresholds tied to specific call categories, not a single global threshold. Complaints get routed to a human within two rings. After-hours overflow gets a callback ticket created in their CRM with the transcript.

SCENARIO / 03Approach

SOC 2 Type II for a Series A B2B SaaS chasing enterprise

Series A team with several enterprise deals blocked on missing SOC 2. No internal security person.

The problem

The team wants to be audit-ready in six weeks. SOC 2 Type II requires an observation period of three to six months minimum. Anyone telling them otherwise is selling them a different framework.

What we’d refuse to do

We would not sell a six-week SOC 2 Type II package. We would not tell them their controls are audit-ready if they are not. We would not take a percentage of the platform license. We would not bundle pen-testing into the scope and recommend ourselves to do it.

First call: scoping

We tell them the realistic timeline on the call before we are hired. Type I is achievable in four to eight weeks. Type II requires the observation period, period. If they need Type II to close the deals, we plan a Type I bridge and start the Type II clock immediately.

Platform choice

Vanta or Drata depending on stack and budget. Partner pricing passed through with no margin from us. Policies written for their actual stack, not the platform's defaults. The platform earns its fee on automation, not on templates.

Evidence collection

Instrument what we can automate. Document what we cannot. Train one of their engineers to own evidence collection going forward. The goal is that they renew Type II next year without us.

Auditor introduction

We will introduce them to two or three auditors we would trust. They choose. We are not the auditor and we never refer to a single one as our preferred to avoid the obvious conflict.

Real engagement scope is written and signed before week one. These walkthroughs are how we think about systems, not promises about specific deliverables. We’ll write a similar approach document for your problem on the first call.

Field notes

Operator writing, in our voice.

All field notes
8 min read

Similarity cannot tell you which fact is current

Plain RAG handles one-off corrections. It fails when an entity has a long history of near-identical facts. A cheap recency lane cuts the stale-fact leak.

Read →
7 min read

Sub-10ms decisioning: where the model isn't

In a real-time decisioning system, the language model is not the thing making the decision. It is the system around the decision. Put it in the hot path and you turn a risk engine into a latency incident.

Read →
9 min read

Your agent's tools are the attack surface

The thing that goes wrong with a production agent is rarely the model saying something rude. It is the model being talked into misusing the tools you handed it. Every tool you give an agent is a permission you give to whatever can talk to it.

Read →
The stack

We're opinionated, but we'll meet your stack where it is.

We don't sell you a proprietary platform. The categories below are what we use most. We track new frontier model releases and most get evaluated for production fit within a couple of weeks. The list is current, not exclusive.

Models
  • Claude (Anthropic)
  • GPT (OpenAI)
  • Gemini (Google)
  • Llama (Meta)
  • Voyage embeddings
  • Cohere rerank
Orchestration
  • LangGraph
  • Mastra
  • Inngest
  • Temporal
  • Trigger.dev
  • n8n (self-hosted)
Voice
  • Retell
  • Vapi
  • Bland
  • Deepgram
  • ElevenLabs
  • Twilio
Data
  • Postgres + pgvector
  • Snowflake
  • Pinecone
  • Turbopuffer
  • Clickhouse
  • dbt
App layer
  • Next.js
  • Remix
  • Hono
  • tRPC
  • Drizzle
  • Prisma
Infra
  • Vercel
  • AWS
  • Cloudflare
  • Fly.io
  • Render
  • Modal
Observability
  • Helicone
  • Langfuse
  • Datadog
  • Sentry
  • OpenTelemetry
  • Grafana
Compliance
  • Vanta (partner)
  • Drata (partner)
  • SOC 2
  • HIPAA
  • GDPR
  • ISO 27001

On something else? Bun, Hono, ScyllaDB, Convex, Pulumi, OpenSearch, Workers AI, your own internal platform. Say so. A decade each in production engineering has touched most of it.

Pricing

One pricing model. Three tiers. No surprises after the contract.

Pick the tier that fits the scope. Inside any tier, we configure the work from the services menu above. Everything is monthly. No annual contracts, no setup fees, no per-seat, no per-workflow. Pause if you need to. Fire us if we're not earning our keep.

Say yes if these are true
  • ✓You have one named decision-maker who can say ‘ship it’ on a Friday.
  • ✓The deliverable has been written down, or you’ll let us write it in week zero.
  • ✓Your roadmap has a real AI thing due this quarter and engineering is at capacity.
  • ✓You’ve watched a vendor pilot die in a demo folder and you’re done with that pattern.
Say no if these are true
  • ✕You don’t have data infrastructure yet. We’re not a data engineering shop.
  • ✕You need a strategy document or a maturity assessment. We ship working software.
  • ✕The deliverable hasn’t been agreed at the leadership level. We don’t work around your CTO.
  • ✕You’re shopping for the cheapest vendor on the page. That isn’t us, and we’d be a bad fit.

Sprint

tier
$13,500/ month

A senior operator embedded, one well-scoped problem, one Slack channel.

Best for: a single agent, a focused integration, an internal tool that needs to exist by next month.

  • ✓Senior operator embedded, ~30 hrs/week
  • ✓Weekly demo, daily Slack
  • ✓Code in your repo, infra in your cloud
  • ✓Weekly written status, no decks
  • ✓Pause or end on 30 days' notice
Start a sprint conversation →

Starts in 7 days. We don't begin until we've named the outcome together.

Most chosen

Pod

tier
$38,500/ month

Full senior team embedded. Technical lead, delivery, on-call. Enough horsepower to ship a system, not a feature.

Best for: an end-to-end build (agents + voice + tooling), or a Series A rebuild, or a 0→1 launch.

  • ✓Senior engineers embedded full-time, founder-led
  • ✓Weekly demo, daily standup in your Slack
  • ✓Hiring plan, infra plan, on-call rotation included
  • ✓Partner pricing passed through (Vanta, Retell, Vercel)
  • ✓Pause or end on 30 days' notice
  • ✓Quarterly written architecture review
Start a pod conversation →

The engagement shape most teams need. Starts in 14 days.

Fleet

tier
Let's talk

Larger scope, longer commitment, carefully-sourced senior contractors added only when the work demands it. Founders stay in the lead seat.

Best for: 3+ concurrent workstreams, a CTO transition, or a portfolio playbook across multiple companies.

  • ✓A founder-led senior team, sourced for the engagement
  • ✓Embedded VP-level technical leadership from one of the founders
  • ✓Quarterly board-ready architecture reviews
  • ✓Custom SLA, custom MSA, custom everything
  • ✓Dedicated security and compliance liaison
Start a fleet conversation →

We will be honest on the first call if we're not the right team. Conversations start with a 45-min scoping call.

The scope of “working prototype” gets written before we start. If you watch Friday's demo and it doesn't match what we agreed on, no invoice for week two. It’s a real commitment, not a sales line; the operating model only works if the artifact we agreed on is the one that lands.

Questions

The questions every client asks us in the first 20 minutes.

Full-time engineers are usually the right answer eventually. We're the bridge. You hire us when you need someone shipping by Monday and a job posting won't close for months. When you've hired the right team, we hand off everything. The code is already yours, the infra is already in your cloud, the runbook is already written. Working ourselves out of the job is the goal, and we don't mind when it happens.

Most are. Our engineers' decade-each in production has touched most stack vintages still in use: Rails monoliths, 4-year-old Next.js apps with three router migrations, greenfield TypeScript. We adapt to your conventions, your CI, your branching model. We won't try to rewrite your stack to use our preferred one. That's a tell of an agency that's actually selling templates.

Real answer: our MSA is signed once, and engagements run on monthly purchase orders. You can pause or end at any time on 30 days' notice. That's not a fine-print clause, it's the operating model. We'd rather stay because we're earning it than because you signed a year of it.

Engineers with a decade each in production across AI, infrastructure, and platform work. Founder-led, with no junior bench to hide behind. You get the people who'd actually be writing the architecture doc anywhere else. We don't post bios on the site because LinkedIn isn't the right hiring surface; we'd rather you meet whoever is staffed on the first call and decide from there.

Sprint engagements: typically 7 days from signature. Pod engagements: 14 days. We won't lie about availability. If taking your engagement would mean starting late or staffing it thin, we'll say 'we can't take you for six weeks' instead, and you can hold us to the date we do give.

Mostly because we don't try to be everything. Six services. Senior engineers only. Code in your repo. Monthly contracts. Weekly demos. Founder-led. No PMs in the loop, no offshore handoff, no proprietary platform. If you've been burned, you know what bit you. We've tried to make ourselves the opposite of that.

Day 1: kickoff call, Slack channel created, repo access exchanged, problem statement written and pinned. Day 2: scoping doc with the smallest shippable thing identified. Day 3–4: working prototype in a sandbox. Day 5: Loom walkthrough, demo on your calendar for next Friday. Week one is choreographed. The improvising starts in week two.

Yes to all three. Vendor security questionnaires are turned around quickly because there's no committee to route around. BAAs are ready for legal review on day one for healthcare engagements. The goal is to be the easiest vendor your procurement team deals with this quarter.

Accepting new engagements

The hard part isn't building the AI.It's shipping it.

Twenty minutes on a call. Bring the messiest problem you have. We'll tell you, on the call, whether we're the right team for it.

20 min
Honest first call
7 days
Sprint engagement starts
Week 1
Working prototype
30 days
Cancel any time, in writing

Prefer email? hello@milebits.tech

We reply within one business day. No drip campaigns, no nurture sequences.