Custom AI systems, agent workflows, data pipelines, and 24/7 operations for operations-heavy and regulated teams. We build it, we deploy it, and we run it — so your team doesn't have to become an AI ops team.
Multi-year engagements running production AI in operations-heavy, regulated environments.
Owned software outlives every SaaS vendor. We build it. We run it.
One senior team handles all of it — the strategy, the build, the deploy, and the day-to-day operations that keep it running after launch.
Owned multi-tenant systems built for your team — with internal AI registries, governance, and per-team controls. Software you keep when we're done.
Example deliverables
Alternative you're weighingHiring a senior platform engineer in-house
The systems that actually do the work day-to-day — intake automation, document review, scheduling, follow-up — wired into your existing stack.
Example deliverables
Alternative you're weighingA generic automation shop stitching together Zapier and ChatGPT
Moving your data where the agents need it: typed schemas, retrieval indexes, and integrations with legacy systems most consultants won't touch.
Example deliverables
Alternative you're weighingA data engineering shop unfamiliar with AI workloads
We run what we build. On-call, monitoring, model updates, skill maintenance — a senior team behind your system around the clock.
Example deliverables
Alternative you're weighingSaaS vendor "support" that closes tickets without solving problems
We build the secure bridges between Claude and your real systems — custom MCP servers, OAuth 2.1 connectors, legacy-API integrations — so your AI runs on your actual data instead of a tidy demo. It's the hard, unglamorous part most "AI shops" skip, and the part that decides whether any of it works once it's live.
Example deliverables
Alternative you're weighingA demo that works on sample data and breaks on yours
We turn your calls, meetings, and transcripts into something you can actually search and act on — the tasks, decisions, risks, and follow-ups pulled out and indexed, so "what did we promise that client in March?" takes seconds, not an afternoon of scrubbing recordings. The conversations that used to disappear start paying you back.
Example deliverables
Alternative you're weighingNotes nobody re-reads and a search box that finds nothing
It's less about your industry than your operations. A few places that pattern shows up — but the list isn't the point.
Where we help
Client intake automation, document and matter review, unifying data trapped in legacy case-management systems, and secure AI access to your systems of record.
Why it's hard
Confidential client data, vendor lock-in, and staff who are (rightly) cautious about AI touching sensitive work.
Our posture
NDAs as standard, least-privilege access, full audit trails, and code you own. We move carefully and we don't put your name anywhere without your say-so.
Where we help
Patient intake, clinical-documentation workflows, eligibility and records pipelines, and operational dashboards that replace expensive rented BI.
Why it's hard
PHI handling, HIPAA obligations, and data fragmented across EHRs and point vendors.
Our posture
BAAs, HIPAA-aligned data handling, and architectures where PHI stays in your environment.
Where we help
Operations and catalog automation across large SKU sets, marketing and attribution dashboards you own, and agent workflows over your Shopify/ads/3PL data.
Why it's hard
Data sprawl across a dozen tools and costly dashboards nobody actually owns.
Our posture
Your data, your platform, no per-seat SaaS tax.
Not on this list? It usually still rhymes. We've built for staffing and recruiting firms, construction and building companies, ad and creative agencies, and plenty of teams that don't fit a neat category. If your operations run on rented tools and manual workflows, the playbook is the same — we just point it at your stack.
24/7 operations isn't a support inbox. It's a senior team behind your platform: watching it, fixing it, and keeping it current.
We run the same operations tooling on our own infrastructure that we deploy for you — continuous monitoring, automated incident response, weekly model and accuracy checks, and a tight enough feedback loop that we catch regressions before your users do. You get monthly reporting and a named senior engineer on call — not a ticket queue and a chatbot.
Anyone can stand up a model. Keeping a platform correct, current, and trusted once real work depends on it — that's the part most shops never sign up for. It's the part we do.
Monthly retainer — per environment or per tenant.
In scope
The platform we built, the agents we deployed, the integrations we own, incident response, model updates, skill maintenance, monthly reporting.
Out of scope
Your help desk, your unrelated SaaS subscriptions, hardware support, IT generalist work.
Overage and incident-response terms are documented in the engagement letter.
| Environment | Status | Last incident | Uptime (this month) |
|---|---|---|---|
| Client A — primary | Healthy | 12 days ago | 99.94% |
| Client B — east | Healthy | 31 days ago | 100% |
| Client C — multi-site | Watching | 2 hours ago | 99.71% |
| Client D — product | Healthy | 8 days ago | 99.98% |
Illustrative — real environments shown to clients only.
Model provider API regression
Summary. At 02:14, a primary model provider's completions endpoint began returning elevated 5xx errors. Affected: 6 agents across two client environments. Detected by our latency monitor within 3 minutes. Mitigated by failover to a secondary provider. Resolved at 03:09.
Timeline
Root cause
A provider-side deployment regressed request validation for a subset of tool-call payloads. On our side: the failover path existed but routed a narrower set of skills than it should have.
What we changed
Client impact. Zero workflow disruption. 41 inbound tasks were rerouted through the secondary provider during the incident and completed within normal SLAs.
Representative example — illustrative, not a specific client incident.
Four stages from renting software you don't control to running a platform you own.
We map what your company currently rents that you should own.
DeliverableA 1-page SaaS replacement map, ranked by leverage and risk.
One end-to-end thing live in production. Not a demo. Not a proof of concept.
DeliverableA working system your team uses every day.
We absorb adjacent SaaS into the platform you own. Registry, governance, and multi-tenant patterns emerge here.
DeliverableAn owned platform replacing 3–5 vendor systems, with internal AI registry and per-team controls.
We run what we built.
DeliverableMonthly ops report, on-call coverage, an evolving roadmap, and quarterly platform review.
Anonymized, client-approved quotes from active engagements.
"We stopped renting our most important workflows. SilverArrows replaced three SaaS tools we'd outgrown with one platform we actually own — and it shipped in weeks, not quarters. Because they run it 24/7, my team never had to become an AI ops team."
— Operations executive, professional-services firm
"Intake used to be the bottleneck across every location. Now it's automated end-to-end and I can see exactly what every agent is doing. When something upstream broke at 2am, they'd already fixed it before we noticed."
— Operations lead, multi-site healthcare practice
"Most shops hand you a demo and disappear. SilverArrows shipped a production system, handed us the code, and stayed on to run it. The registry alone caught three overlapping AI tools we didn't realize we were paying for."
— Founder, consumer brand
What you actually get, head to head.
| SilverArrows | In-house hire | Traditional dev shop | Generic AI shop | Big consultancy | |
|---|---|---|---|---|---|
| Time to first production system | 4–6 weeks | 3–6 months | 2–4 months | 1–2 weeks | 3–9 months |
| Code ownership | You | You | You | Often them | You (eventually) |
| Senior engineer on every project | Yes | N/A | Sometimes | No | No |
| 24/7 operations included | Yes | Hire separately | No | Locked-in vendor | Yes (expensive) |
| Can absorb existing SaaS | Core competency | Depends | Maybe | No | Yes |
| Minimum engagement | $$ | $$$$ (salary+benefits) | $$ | $ | $$$$$ |
A small senior team. The principal is on every engagement. We grow by depth, not by headcount.
Code escrow, runbooks, and documented handoff terms. Your platform survives us — that's the point of owning it.
No. The cross-pollination across clients is part of the value. If you want full-time AI engineering inside your company, we can help you hire it.
We start with a paid pilot — one system live in production, typically 4–6 weeks — then a monthly retainer per environment. We don't take engagements smaller than that; below it, you're better served by tools you can buy off the shelf.
Yes. Both. Standard practice.
Only with your sign-off. We lead with anonymized results, so nothing identifying goes public unless you want it to. If you're open to being a named reference, you approve every word first.
You do. From day one.
Those are fine for prototypes. They break under production load, can't be observed properly, and become their own vendor lock-in. We build owned systems that don't.
Severity-1 incidents get a response within 30 minutes, 24/7/365. The full operations model is on the live-ops window.
BAA available. HIPAA-aware data flows. We've shipped under it.
Most teams are renting AI tooling from vendors who will raise prices, change terms, or get acquired. Our worldview: AI tooling is too core to outsource. We help you own it.
Most companies trying to adopt AI start in the wrong place.
They go looking for the next tool — the latest app, the newest subscription, the AI product everyone's talking about. Six months later they're paying for software no one really uses, because the tool was never the thing that was missing.
I build the thing that was actually missing.
I've been writing software since I was a kid, and I spent more than twenty years building products and leading engineering teams as a CTO. The whole time, I watched companies buy more software than they needed, stitch together dozens of systems, and slowly lose control over how their business actually operates.
That's why I started SilverArrows.
Today I build custom AI platforms, intelligent workflows, and automation infrastructure that businesses own. Not another subscription. Not another dashboard. Real software designed around the way a company actually works.
Over the years I've found that the companies seeing the biggest return from AI all follow the same pattern.
They start with the boring work, not the flashy demos — the repetitive tasks everyone avoids: customer intake, call summaries, document processing, back-office operations, and information that has to move from one place to another.
They understand the workflow before they automate it. Speeding up a broken process just creates problems faster.
They keep people involved where judgment matters. AI handles the repetitive work so humans can focus on the decisions that need experience, context, and accountability.
And they start small. One workflow. One measurable improvement. Once people feel what good AI is like, adoption happens on its own.
I'm not interested in helping companies "use AI." I'm interested in helping them build systems that become part of how their business operates for years to come.
If you're wondering where AI could make the biggest impact in your company, it's usually simpler than you think. Start with the task someone on your team wishes they never had to do again.
Book a 30-minute AI systems audit, or tell us about your stack below. We reply within one business day.
Async — tell us about your stack
$ deploy silverarrows --env=prod✓ inference pipeline ready✓ secure data vault mounted✓ compliance checks passed✓ latency 220ms p95 — shipping it
This is the boring part. We make it look easy.
Full 60–90s product walkthrough — coming soon.
Categories, not clients. We never name a vendor mid-deal.
We don't integrate these. We replace them with something you own.
Anonymized, but real — platforms running in production across legal, healthcare, and ecommerce.
Want the full walkthrough — architecture, code, and the numbers behind these? We'll go deep under NDA on a call.
Field notes, monthly. One essay, one short note, one link. No promo.
Most companies I talk to are renting the software their business runs on. For a long time that trade made sense: someone else hosts it, patches it, and answers the pager at 3am, and you pay a predictable monthly fee. You give up control, but control felt like someone else's problem.
Then the bill stops being predictable. Per-seat pricing creeps up every renewal. A vendor changes its terms, gets acquired, or quietly deprecates the one feature an entire workflow depended on. The "predictable monthly fee" turns out to be a lever someone else is holding, and the more critical the tool, the harder that lever is to refuse.
AI made all of this worse, fast. In the span of a year a typical company goes from zero AI tools to ten of them, scattered across five departments, bought on five different cards. No inventory. No governance. No one who can say, with confidence, what they all do, what data they touch, or where two of them quietly overlap. The thing that was supposed to create leverage created sprawl.
The standard answer is to bring in an outside shop. Most of them fail, and they fail the same way: they ship a demo, hand you a brittle automation, and leave. What they built isn't observable, isn't owned, and isn't operated by anyone once the invoice clears. Six months later it's just another vendor you can't fire — except this one you paid to build.
I think the framing is wrong. We keep treating AI tooling as a feature you buy. But if an agent is handling your intake, reviewing your documents, or running your scheduling, that isn't a feature — it's infrastructure. And infrastructure that core to how a business runs shouldn't be rented from someone whose incentives stop aligning with yours the moment the contract is signed.
"Owned" is concrete, not ideological. It means the code is yours from day one. It means a registry — a real inventory of every AI system running, what it does, who owns it, and where it overlaps with something else — so the sprawl never gets away from you again. It means governance and per-team controls built in, not bolted on. And it means you can fire us and keep running, because the platform was built to outlive the people who built it.
Here's the part nobody likes to hear: the model is the easy part. What's hard is everything around it. The failover when a provider regresses at 2am. The evaluation when an upstream API silently changes shape. The runbook for the incident that happens while everyone's asleep. Most shops skip that work because it doesn't demo well — and it's also the only part that matters once the thing is actually live and your business depends on it.
So that's the case, plainly: the AI systems running your operations are infrastructure, infrastructure should be owned, and owning it only works if someone runs it like they mean it. Build the platform. Keep the code. Run it around the clock. Rent the things that don't matter — and own the ones that do.
OC