Carrier-grade OSS/BSS, network operations, and customer care.
We build and modernize the software that runs telecom: order-to-activate and billing (BSS), provisioning and service assurance (OSS), network-operations analytics, and carrier-scale support.
It’s engineered for the reliability your subscribers assume and shipped behind staged rollouts with rollback. Built in your own cloud, every line assigned to you — production in 4–8 weeks.
Because a carrier’s stack is decades of accreted systems carrying live traffic that can never go dark. Billing engines, provisioning workflows, and inventory databases were built in silos, integrated by batch jobs, and wrapped in so much risk that a routine change becomes a quarter-long project — some provisioning cycles still run on the order of weeks.
Meanwhile 5G, IoT, and new monetization models demand orchestration the legacy stack was never designed for. The cost of getting a change wrong is uniquely brutal here.
The FCC traced the February 2024 AT&T outage to insufficient post-installation testing and change controls. That is the telecom software problem in one incident: the systems are mission-critical, the blast radius is national, and the engineering discipline around how change ships is the whole game.
Telecom isn’t one system; it’s a stack of high-volume processes, each with its own failure mode and its own payoff. For each, what we build, the benefit it produces, and how that plays out:
Software that activates services, manages inventory, and detects and correlates faults across the network. Benefit — faster activation and fewer truck rolls, with problems caught before subscribers notice. Order-to-activate cycles measured in weeks compress toward minutes, and assurance tooling turns reactive firefighting into proactive resolution.
For example, a new business-fiber order that once waited days on a manual provisioning queue activates the same day through an orchestrated workflow — and a degrading cell site is flagged and rerouted before the support line lights up.
Rating, charging, billing, and order-to-cash systems that price complex plans and bill them correctly. Benefit — accurate bills, faster new-plan launches, and less revenue leakage. Modern charging lets product teams launch a new tariff in days instead of waiting on a release train, and consistent rating cuts the disputes that flood the call center.
For example, a usage-based 5G plan goes live for a launch window instead of missing it by a quarter — and a charging error that would have triggered thousands of billing complaints is caught in test against real rating scenarios.
Telemetry pipelines and models over network and device data for capacity planning, anomaly detection, and predictive maintenance. Benefit — higher uptime and capital spent where it actually relieves congestion. Operators see degradations forming and act before an outage, and plan capacity against real demand instead of guesswork.
For example, an anomaly model flags a backhaul link trending toward saturation a week out, so capacity is added before peak — turning what would have been a customer-visible brownout into a non-event.
Support assistants and self-service flows wired to billing, provisioning, and account systems so they resolve — not just chat. Benefit — routine contacts handled instantly at lower cost, agents freed for the hard cases. Plan changes, billing questions, and outage status are answered in seconds, and call volume stops queuing behind agents.
For example, a customer checking “why is my bill higher this month?” at 11 p.m. gets a grounded, itemized answer from their own account data instead of waiting for business hours — and the agent queue shortens for the disputes that genuinely need a person.
Self-service mobile/web apps for subscribers and dispatch/field tools for technicians, integrated to the core stack. Benefit — lower service friction for customers and more first-time-fix for field teams.
For example, a technician arrives with the full service history and the right part already dispatched, so the visit resolves on the first trip instead of a repeat.
The scope below is what separates a system that carries live subscriber traffic reliably from one that becomes the next outage post-mortem.
We build and integrate order-to-activate, charging, billing, provisioning, inventory, and assurance — through governed, permissioned APIs across your existing systems rather than another silo. The honest “modernize this, leave that” call is part of the scope.
We re-platform aging billing and provisioning systems toward cloud-native, API-first architectures without taking the service offline — the same application modernization and legacy migration discipline we’ve run on production systems for over a decade, paying down technical debt one pass at a time.
Telemetry pipelines, anomaly detection, and predictive-maintenance models over your network and device data — scoped first against where AI genuinely pays via our AI readiness assessment, with the “don’t build this one” call included.
Support assistants and self-service flows grounded in your account, billing, and provisioning data, with human-in-the-loop escalation designed in so the system escalates instead of guessing when confidence drops.
Staged rollout, automated testing, regression prevention, and rollback built into how every change ships — plus the DevOps and observability to catch a bad change before it propagates. This is the layer the AT&T post-mortem was about.
Customer-facing web/mobile apps and field-technician tooling, integrated end-to-end to the OSS/BSS core.
What you get when you hire us — all assigned to you
One accountable lead, fixed scope, no handoffs — tuned for systems where downtime is not an option.
Map the systems, integrations, and live traffic the change touches, and the constraints — regulatory, SLA, peak windows — it must respect.
Output: a ranked plan & the success metrics
Architect the build or migration path, choose the model on your workload where AI is in scope, and define the rollout, testing, and rollback strategy before code.
Output: an architecture & a safe-change plan
Develop in your own cloud tenant, wired to your systems through governed APIs, with assurance, guardrails, and observability in place.
Output: a working system behind your access controls
Shadow, then a contained pilot, then a staged rollout with rollback at each gate — metrics watched the whole way, your team trained to operate it.
Output: a production system & a team that owns it
Production in 4–8 weeks, full IP assignment signed at kickoff, build cost fixed-scope and run cost modeled before we build — so the first invoice is a forecast you’ve already seen (cost guide).
We have not built for a named carrier, and we won’t pretend otherwise. What we can point to is the discipline that telecom software lives or dies on — shipping change to a mission-critical, always-on system without breaking it — proven on production work over more than a decade.
For four years we’ve held a 200+ location business at twice-a-week releases with zero critical defects, by restructuring how change flows: smaller units of work, pre-release quality and regression prevention, staged rollout, and continuous production monitoring (BJ’s Restaurants — a restaurant chain, cited here as a cross-industry example of release-safety discipline, not a telecom engagement). That is precisely the muscle the AT&T post-mortem found missing: post-installation testing and change controls.
We’ve also carried a production platform through 12+ years of modernization and migration without downtime (Bridge Athletic) — the same never-go-offline constraint a carrier core runs under.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, including multimillion-dollar systems for one of the world’s largest manufacturers, and personally accountable for every engagement. We’ll tell you plainly when a rebuild is the wrong call — which a vendor paid to sell one won’t.
We treat safe change as the product. For systems where a bad deploy is a national incident, the testing, staged rollout, and rollback discipline isn’t an add-on — it’s the core of what we deliver.
Responsible AI is the founding charter. Where AI touches subscriber data, network decisions, or billing, governance — what it may do, when a person must be in the loop, how it’s audited — is engineered in, not bolted on.
Engine-agnostic. Where AI is in scope, we benchmark OpenAI, Claude, and Gemini on your actual workload and route to whichever wins. No partnership steers the recommendation.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes it answers for it.
Built to transfer. Code, pipelines, models, and runbooks are assigned to you, and your team is trained to run and extend the system when we step back.
What teams want to know before they let us touch a system that can never go dark.
We have not delivered for a named carrier, and we won’t claim a case we don’t have. What transfers directly is the discipline telecom demands — shipping change to a mission-critical, always-on system safely. We’ve held a 200+ location business at twice-a-week releases with zero critical defects for four years, and carried another production platform through 12+ years of modernization without downtime. We scope conservatively, prove the approach on a contained pilot before wide rollout, and our founder is accountable for the result.
Yes — that never-go-offline constraint is the default we engineer to. We re-platform aging billing and provisioning systems incrementally toward cloud-native, API-first architectures, running new and old in parallel and migrating behind staged cutover with rollback at each step. The work is sequenced so service continuity is the gate every change has to clear, not an afterthought.
By making safe deployment part of the build, not a hope at the end. Every change ships through automated testing and regression prevention, shadow and pilot stages before wide rollout, observability that catches a bad change as it propagates, and rollback at each gate. The February 2024 AT&T outage — 92 million blocked calls from one misconfigured element — is exactly the failure the FCC traced to missing post-installation testing and change controls, and exactly what this discipline exists to prevent.
Both, scoped to what genuinely pays. We build network-operations analytics, anomaly detection, and grounded customer-care assistants, and we integrate them through governed, permissioned APIs into your stack. We benchmark the candidate models on your real workload rather than defaulting to one, and we’ll tell you where a model isn’t the right tool.
Systems run in your own cloud tenant under your access controls; integrations use scoped, permissioned API calls; and every engagement starts with an NDA and a security review. We document every data path so your security and compliance teams verify rather than trust, and we design subscriber-data and lawful-intercept-adjacent flows conservatively. For the broader compliance posture we apply to regulated data, see how we work in fintech software development.
You do — completely. Code, integrations, analytics pipelines, models, evaluation suites, and runbooks transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend them. Keep us on a reduced retainer or take the keys; the engagement is built around the handover.
Most engagements reach production in 4–8 weeks under a fixed-scope contract with one accountable lead and payment tied to ROI. Build cost depends on scope — our AI development cost guide gives real ranges — and where there’s an ongoing run cost we model it before building, so the first invoice is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Bring the system — OSS/BSS, network analytics, billing, or care — and we’ll tell you honestly what it takes to build or modernize it safely, what it costs, and how we’d ship it without risking the network.