A Software Engineer at Propio plays a pivotal role in bridging communication gaps worldwide. Propio is a leading provider of technology-enabled language services, specializing in over-the-phone, video, and in-person interpretation, as well as translation and localization. As an engineer here, you will design, build, and maintain the highly available, real-time systems that instantly connect clients with professional interpreters. Every line of code you write directly impacts critical sectors like healthcare, government, and emergency services where timely communication is a matter of life and death. The engineering team at Propio tackles complex challenges involving real-time audio and video routing, high-throughput queue management, and robust platform orchestration. Whether you join as a Service Management Platform Engineer, a Software Development Engineer II (SDE II), or a Software Development Engineer III (SDE III), your work will focus on scalability, low-latency performance, and system resilience. You will collaborate with cross-functional teams to modernize legacy monoliths into cloud-native microservices, ensuring that the platform remains reliable under heavy, unpredictable loads. This role is ideal for engineers who thrive in fast-paced, mission-driven environments. At Propio, you will not just be building software; you will be engineering a lifeline for millions of limited-English-proficient individuals.
Initial Screening
reportedAlign on background and expectations with the recruitment team.
What to demonstrate
- Align on background and expectations with the recruitment team
- Depth in Service Management Platforms
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Technical Assessment
reportedTest practical coding skills through a technical evaluation.
What to demonstrate
- Test practical coding skills through a technical evaluation
- Depth in Service Management Platforms
How to prepare
- Answer aloud and timed: Implement a rate-limiting algorithm to protect a public-facing API gateway from denial-of-service attacks.
- Answer aloud and timed: Find the longest common prefix among an array of strings representing localized language codes.
Virtual Onsite Loop
reportedEngage in system design, technical discussions, and behavioral scenarios.
What to demonstrate
- Engage in system design, technical discussions, and behavioral scenarios
- Depth in Service Management Platforms
How to prepare
- Answer aloud and timed: Optimize an algorithm to find the nearest available interpreter based on geolocation coordinates.
- Answer aloud and timed: Design a real-time, low-latency audio and video routing platform capable of handling thousands of concurrent connections.
PracHub editorial advice for the preparation topics above.
Emphasize Reliability
Propio's software is used in life-critical situations like emergency medical interpreting. Always highlight how you design for reliability, monitoring, and fast recovery in your system design and coding answers.
Think Out Loud
During coding and architectural rounds, talk through your thought process. Interviewers care just as much about how you arrive at a solution, how you handle constraints, and how you receive feedback as they do about the final code.
Prepare STAR Stories
For behavioral questions, structure your answers using the Situation, Task, Action, and Result (STAR) framework. Be specific about your individual contribution and the measurable impact of your work.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Find the longest common prefix among an array of strings representing localized language codes.
Find the longest common prefix among an array of strings representing localized language codes.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Track a rolling failure rate per destination for circuit decisions
The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.
Approach
- Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
- Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
- State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
- Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
- The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
- A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?
Canonicalise a request body into a stable idempotency fingerprint
idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.
Approach
- Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
- Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
- Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
- Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.
Follow-up
- A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
- Where does the fingerprint get computed relative to request decompression and the body-size limit?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
Given a stream of incoming call requests, design a queue system that prioritizes emergency calls over standard
Given a stream of incoming call requests, design a queue system that prioritizes emergency calls over standard translation requests.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Write a function to detect cycles in a microservice dependency graph.
Write a function to detect cycles in a microservice dependency graph.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Implement a rate-limiting algorithm to protect a public-facing API gateway from denial-of-service attacks.
Implement a rate-limiting algorithm to protect a public-facing API gateway from denial-of-service attacks.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Optimize an algorithm to find the nearest available interpreter based on geolocation coordinates.
Optimize an algorithm to find the nearest available interpreter based on geolocation coordinates.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Design a real-time, low-latency audio and video routing platform capable of handling thousands of concurrent c
Design a real-time, low-latency audio and video routing platform capable of handling thousands of concurrent connections.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How would you design a distributed notification system that alerts interpreters of new service requests via SM
How would you design a distributed notification system that alerts interpreters of new service requests via SMS, push notifications, and email?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Design a metric and logging aggregation system to monitor the health and performance of distributed microservi
Design a metric and logging aggregation system to monitor the health and performance of distributed microservices.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How would you architect a database schema to support multi-tenant billing and usage tracking for enterprise he
How would you architect a database schema to support multi-tenant billing and usage tracking for enterprise healthcare clients?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Design a failover strategy for a critical database cluster to ensure zero data loss and minimal downtime durin
Design a failover strategy for a critical database cluster to ensure zero data loss and minimal downtime during an outage.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Edge instances grow 400 MB per hour until the nightly restart
Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.
Approach
- Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
- Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
- Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
- The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.
Follow-up
- Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
- How would you have detected this before an OOM, given the nightly restart masked the trend?
Built from the rounds and topics Propio candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Propio loop
- Write out the reported sequence: Initial Screening, Technical Assessment, Virtual Onsite Loop.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.
02Work Service Management Platforms
- Spend the session on Service Management Platforms, which Propio candidates report being tested on.
- Write one worked example in Service Management Platforms and time yourself on it.
Deliverable: One timed worked example in Service Management Platforms.
03Work Platform Engineering
- Spend the session on Platform Engineering, which Propio candidates report being tested on.
- Write one worked example in Platform Engineering and time yourself on it.
Deliverable: One timed worked example in Platform Engineering.
04Work Software Engineering
- Spend the session on Software Engineering, which Propio candidates report being tested on.
- Write one worked example in Software Engineering and time yourself on it.
Deliverable: One timed worked example in Software Engineering.
05Answer out loud: Coding & Algorithmic Problem Solving
- Answer aloud, timed: Given a stream of incoming call requests, design a queue system that prioritizes emergency calls over standard translation requests.
- Answer aloud, timed: Write a function to detect cycles in a microservice dependency graph.
Deliverable: Spoken answers to 2 reported Coding & Algorithmic Problem Solving question(s), under time.
06Answer out loud: System Design & Architecture
- Answer aloud, timed: Design a real-time, low-latency audio and video routing platform capable of handling thousands of concurrent connections.
- Answer aloud, timed: How would you design a distributed notification system that alerts interpreters of new service requests via SMS, push notifications, and email?
Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.
07Answer out loud: Behavioral & Leadership
- Answer aloud, timed: Describe a time when you had to make a difficult technical trade-off to meet a critical business deadline.
- Answer aloud, timed: Tell me about a time you disagreed with a product manager's requirements and how you resolved the conflict.
Deliverable: Spoken answers to 2 reported Behavioral & Leadership question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Describe a time when you had to make a difficult technical trade-off to meet a critical business deadline.
Describe a time when you had to make a difficult technical trade-off to meet a critical business deadline.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Tell me about a time you disagreed with a product manager's requirements and how you resolved the conflict.
Tell me about a time you disagreed with a product manager's requirements and how you resolved the conflict.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Give an example of a complex technical issue you diagnosed and resolved in a production environment.
Give an example of a complex technical issue you diagnosed and resolved in a production environment.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you mentor junior engineers and foster a culture of continuous learning within your team?
How do you mentor junior engineers and foster a culture of continuous learning within your team?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe a situation where you took ownership of a project that was failing and successfully turned it around.
Describe a situation where you took ownership of a project that was failing and successfully turned it around.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
- 01
Describe a time when you had to make a difficult technical trade-off to meet a critical business deadline.
- 02
Tell me about a time you disagreed with a product manager's requirements and how you resolved the conflict.
- 03
Give an example of a complex technical issue you diagnosed and resolved in a production environment.
- 04
How do you mentor junior engineers and foster a culture of continuous learning within your team?
What is the primary technology stack at Propio?
While Propio utilizes a variety of modern technologies, the core backend services are primarily built using C#/.NET and Python, hosted on AWS. Front-end applications typically leverage modern JavaScript frameworks. However, they value strong engineering fundamentals over specific language syntax and welcome candidates from diverse language backgrounds.
Propio Software Engineer candidate reports ↗How does Propio evaluate system design for senior roles?
For SDE III and Platform Engineer roles, the system design round is highly critical. Interviewers look for architectural maturity, the ability to justify design decisions, and a deep understanding of trade-offs regarding scalability, latency, and cost.
Propio Software Engineer candidate reports ↗What is the work model (remote/hybrid) for engineers at Propio?
Many engineering positions at Propio are based out of or aligned with the Overland Park, KS office, but they support flexible hybrid and remote working arrangements depending on the specific team and role requirements.
Propio Software Engineer candidate reports ↗What sets successful candidates apart during the interview process?
Successful candidates demonstrate strong technical curiosity, a passion for solving real-world communication barriers, and a pragmatic approach to software design. They write clean, testable code and can clearly articulate the business value of their technical decisions.
Propio Software Engineer candidate reports ↗What topics does Propio test in interviews?
Propio interviews most often cover Risk Management, LLM Safety, Project Management, Service Management Platforms, and Implementation Management. The exact emphasis depends on the specific role you apply for.
Propio Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Propio Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
Cognition Software Engineer Interview Guide
What Cognition asks Software Engineer candidates, from reported rounds and questions.
Discord Software Engineer Interview Guide
Database practice for Discord candidates: how an index changes a query plan, and which anomalies survive a given isolation level, since read committed still permits non-repeatable reads.
CoreWeave Software Engineer Interview Guide
Failure-handling practice for CoreWeave software engineer candidates: requests whose outcome a timeout leaves unknown, where a retry is safe, and deduplicating at the receiver instead of assuming exactly-once delivery.
Aerospike Software Engineer Interview Questions & Guide 2026
Aerospike database-engine preparation through an original single-owner Python key-value store with bounded logical index accounting, disk-offset reads, conditional writes, tombstones, process-restart recovery and 3,240 checks. Existing 14 p