Custom systems, agent workflows, data pipelines, and the 24/7 operations behind them, for operations-heavy and regulated teams. We build it, we ship it, and we keep it running, so nobody on your team has to turn into an AI ops person.
Multi-year engagements running production AI in operations-heavy, regulated environments.
Owned software outlives the vendor you bought it from.
Shipped resultsThree we lead with: the owned platform, the agents that do the work, and the operations that keep them running. The same team does the strategy, the build, the deploy, and the day-to-day operations that keep it running after launch.
Multi-tenant systems built for your team, with an internal AI registry, governance, and per-team controls. You keep the software when we're done.
Example deliverables
What people do insteadHiring a senior platform engineer in-house
The systems that do the work day to day: intake, document review, scheduling, follow-up, wired into the stack you already have. We build Electronic Performance Support (EPSS) into them, so the guidance a person needs shows up inside the workflow at the moment of the task.
Example deliverables
What people do insteadA generic automation shop stitching together Zapier and ChatGPT
We run what we build: on-call, monitoring, model updates, and skill maintenance, around the clock.
Example deliverables
What people do insteadSaaS 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. Your AI runs on your data instead of a tidy demo set. This is the unglamorous part most AI shops skip, and it's the part that decides whether any of it holds up once it's live.
Example deliverables
What people do insteadA demo that works on sample data and breaks on yours
Getting your data where the agents need it: typed schemas, retrieval indexes, and integrations with the legacy systems most shops won't touch.
Example deliverables
What people do insteadA data engineering shop unfamiliar with AI workloads
We turn your calls, meetings, and transcripts into something you can search and act on. Tasks, decisions, risks, and follow-ups get pulled out and indexed, so "what did we promise that client in March?" takes seconds instead of an afternoon of scrubbing recordings.
Example deliverables
What people do insteadNotes nobody re-reads and a search box that finds nothing
It's less about your industry than your operations. Here are a few places that pattern shows up, though 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.
How we handle it
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.
How we handle it
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 expensive dashboards nobody owns.
How we handle it
Your data on your own platform, without paying per seat for it.
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.
We build custom AI systems for agencies and for the primes who serve them, and we operate them after they go live. The company data a contracting officer asks for is below.
Download capability statement (PDF) →
What we do for agencies. Agent workflows for intake, review, and routing. Electronic Performance Support (EPSS) that puts the checklist inside the task instead of in a binder nobody opens. MCP servers and connectors into systems of record. Data pipelines out of legacy systems that have no modern API. 24/7 operations with a Sev-1 response inside 30 minutes.
Contracting officers and primes: email Omri directly. He answers it himself.
24/7 operations means a senior engineer is watching your platform, fixing it when it breaks, and keeping it current as the models and the APIs underneath it move.
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 feedback loop tight enough to catch regressions before your users do. You get monthly reporting and a named senior engineer on call, rather than a ticket queue.
Standing up a model takes an afternoon. Keeping a platform correct and current once real work depends on it is the part most shops never sign up for. We sign up for it.
Monthly retainer, per environment or per tenant.
In scope
The platform we built, the agents we deployed, the integrations we own, plus incident response, model updates, skill maintenance, and 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 are shown to clients only.
Model provider API regression
Summary. At 02:14 a primary model provider's completions endpoint started returning elevated 5xx errors, affecting 6 agents across two client environments. Our latency monitor caught it within 3 minutes. We failed over to a secondary provider and resolved it 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, not a specific client incident.
Four stages from renting software you don't control to running a platform you own.
We map what you're currently renting and what's worth owning instead.
DeliverableA one-page SaaS replacement map, ranked by risk and payoff.
One complete thing live in production, used by real people, instead of a demo.
DeliverableA working system your team uses every day.
We pull adjacent SaaS into the platform you own. This is where the registry, the governance, and the multi-tenant patterns get built.
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
The same questions, answered for each option.
| 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'd rather go deeper than hire faster.
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, usually 4–6 weeks. After that it's a monthly retainer per environment. We don't take engagements smaller than that. Below it, you're better off with 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 systems you own 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.
Yes. We're a small business under NAICS 541511, 541512, 541519, and 541611. SAM.gov registration is in progress. The has our company data and a capability statement to download.
Most teams rent their AI tooling from vendors who will raise prices, change terms, or get acquired. We think it sits too close to the middle of your business for that. So 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 call, or email me. Either way it comes straight to me and I answer it myself, usually the same day.
Government buyer? Download the capability statement (PDF).
Looking for work instead?
Or use the form
We're a small senior team. We build AI platforms our clients own, then run them 24/7. We're adding two people: one who builds the systems, one who brings mid-market and enterprise ops teams onto them.
Neither role is locked to a fixed shape. Tell us what you want it to look like and we'll work from there.
You'd build the systems on our list: agent workflows, MCP servers and connectors into clients' systems of record, and data pipelines out of legacy software that has no real API. You own features from design through deploy, and then keep them alive, because we operate everything we ship.
What we're looking for
You'd hate this if
You want a narrow ticket queue or a big team to disappear into. And if you think the model is the hard part: most of this job is plumbing, and the plumbing is the reason any of it works.
Compensation: competitive, based on experience.
Ops leaders at mid-market and enterprise companies are paying the highest tax for SaaS sprawl and manual work, and most AI shops sell them demos. Your job is to find them, get them on a call, and bring the right ones onto our platform. You'd own sourcing, the first call, and the close. The founder joins the technical conversations. You don't need to be an engineer, but you do need to be genuinely curious about what we build.
What we're looking for
You'd hate this if
You need an established brand, steady warm inbound, or a script to read. None of that exists here yet.
Compensation: base plus uncapped commission.
One form, both roles. Skip the cover letter, the résumé already covers what you've done. We'd rather hear who you are and what makes you good at this. We read every application and reply either way.
Your résumé goes to us and nowhere else. No recruiters, no lists.
$ deploy silverarrows --env=prod✓ inference pipeline ready✓ secure data vault mounted✓ compliance checks passed✓ latency 220ms p95 — shipping it
This is the boring part. It's also the part that keeps working.
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. These are platforms running in production across legal, healthcare, and ecommerce.
Want the full walkthrough, including architecture, code, and the numbers behind these? We'll go through it under NDA.
Field notes, monthly. One essay and a couple of links, and nothing else.
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. That predictable monthly fee turns out to be a lever someone else is holding, and the more you need the tool, the harder the lever is to refuse.
AI made this worse, fast. In about a year a typical company goes from zero AI tools to ten, scattered across five departments and bought on five different cards. Nobody keeps an inventory, nobody governs it, and nobody can say with confidence what all ten do, what data they touch, or where two of them quietly overlap. The thing that was supposed to create leverage created sprawl.
The usual answer is to bring in an outside shop, and most of them fail the same way. They ship a demo, hand over a brittle automation, and leave. What they built can't be observed, isn't owned by anyone, and isn't operated by anyone once the invoice clears. Six months later it's one more vendor you can't fire, except you paid to build this one.
The mistake is treating AI tooling as a feature you buy. If an agent handles your intake, reviews your documents, or runs your scheduling, that isn't a feature. It's infrastructure. And infrastructure that close to how the business works shouldn't be rented from a company whose incentives stop matching yours the day the contract is signed.
Owned is a concrete word here, not an ideological one. 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 rather than bolted on afterward. And it means you can fire us and keep running, because the platform was built to outlive the people who built it.
The model is the smallest part of this. The rest is the failover when a provider regresses at 2am, the evaluation when an upstream API silently changes shape, and the runbook for the incident that happens while everyone is asleep. Most shops skip that work because it doesn't demo well. It's also the only part that matters once your business depends on the thing.
So, plainly: the AI systems running your operations are infrastructure, infrastructure should be owned, and owning it only works if somebody runs it properly. Rent the things that don't matter. Own the ones that do.
OC