A1BRELLA — Home

const studio = await a1brella.init({ region: "eu-west", seed: 42 })

const agent = studio.spawn({ role: "planner", model: "orakel-v2" })

agent.observe(worldState, { channels: ["sensor", "ops", "human"] })

const plan = agent.plan({ horizon: 12, budget: tokens(80_000) })

for (const step of plan.steps) await step.execute({ dryRun: false })

if (plan.confidence < 0.92) agent.replan({ strategy: "beam", k: 5 })

telemetry.trace("plan.settled", { id: plan.id, latencyMs: 142 })

const memory = new VectorStore({ dim: 1536, metric: "cosine" })

await memory.upsert(chunk.embedding, { ttl: "7d", src: "journal" })

const recall = await memory.query(intent, { k: 8, minScore: 0.72 })

ctx.inject(recall.map(r => r.text).join("\n"), { role: "system" })

export default pipeline(observe, plan, act, reflect, checkpoint)

const critic = studio.spawn({ role: "critic", temperature: 0.2 })

critic.review(plan).then(patch => merge(patch, { atomic: true }))

await handoff(agent, human, { reason: "edge_case", urgency: "low" })

studio.on("drift", metrics => recalibrate(metrics, { decay: 0.9 }))

const guard = compose(piiFilter, jailbreakProbe, toxicityGate)

router.route(intent, { fallback: human, timeoutMs: 30_000 })

await journal.append({ actor: agent.id, action: plan.summary })

scheduler.every("5m", () => agent.sync({ source: "worldState" }))

const studio = await a1brella.init({ region: "eu-west", seed: 42 })

const agent = studio.spawn({ role: "planner", model: "orakel-v2" })

agent.observe(worldState, { channels: ["sensor", "ops", "human"] })

const plan = agent.plan({ horizon: 12, budget: tokens(80_000) })

for (const step of plan.steps) await step.execute({ dryRun: false })

if (plan.confidence < 0.92) agent.replan({ strategy: "beam", k: 5 })

telemetry.trace("plan.settled", { id: plan.id, latencyMs: 142 })

const memory = new VectorStore({ dim: 1536, metric: "cosine" })

await memory.upsert(chunk.embedding, { ttl: "7d", src: "journal" })

const recall = await memory.query(intent, { k: 8, minScore: 0.72 })

ctx.inject(recall.map(r => r.text).join("\n"), { role: "system" })

export default pipeline(observe, plan, act, reflect, checkpoint)

const critic = studio.spawn({ role: "critic", temperature: 0.2 })

critic.review(plan).then(patch => merge(patch, { atomic: true }))

await handoff(agent, human, { reason: "edge_case", urgency: "low" })

studio.on("drift", metrics => recalibrate(metrics, { decay: 0.9 }))

const guard = compose(piiFilter, jailbreakProbe, toxicityGate)

router.route(intent, { fallback: human, timeoutMs: 30_000 })

await journal.append({ actor: agent.id, action: plan.summary })

scheduler.every("5m", () => agent.sync({ source: "worldState" }))

const studio = await a1brella.init({ region: "eu-west", seed: 42 })

const agent = studio.spawn({ role: "planner", model: "orakel-v2" })

agent.observe(worldState, { channels: ["sensor", "ops", "human"] })

const plan = agent.plan({ horizon: 12, budget: tokens(80_000) })

def train(model, corpus, epochs=64, lr=3e-4, warmup_steps=500):

for batch in loader(corpus, shuffle=True, batch_size=128):

loss = mse(model(batch.x), batch.y) + l2_penalty(w, 1e-5)

opt.apply(tape.gradient(loss, w), clip_norm=1.0, lr=lr)

if loss < 1e-3: checkpoint(model, tag=f"epoch_{epoch:03d}")

sampler = TopK(k=40, temperature=0.8, repetition_penalty=1.1)

tokens = model.generate(prompt, max_len=256, stream=True)

yield from stream(tokens, on_token=lambda t: emit("delta", t))

suite = EvalSuite.load("humans_and_machines", split="holdout")

report = suite.run(model, seed=1337, workers=8, verbose=False)

assert report.pass_rate > 0.99, f"regression: {report.failures}"

embed = encoder(text, normalize=True, pooling="mean", dim=1536)

index.add(embed, meta={"src": "journal", "ts": now().isoformat()})

df = df.dropna().pipe(tokenize).pipe(dedupe).cache("corpus_v3")

wandb.log({"loss": loss, "step": step, "lr": sched.get_last_lr()})

model.push("a1brella/orakel-v2", private=True, card=MODEL_CARD)

probe = LinearProbe(layer=-2).fit(activations, labels, l1=1e-4)

attention = rollout(attn_maps, discard_ratio=0.9, head="mean")

dataset = interleave(synthetic(0.3), curated(0.7), seed=1337)

distill(teacher=orakel_xl, student=orakel_mini, kl_weight=0.5)

def train(model, corpus, epochs=64, lr=3e-4, warmup_steps=500):

for batch in loader(corpus, shuffle=True, batch_size=128):

loss = mse(model(batch.x), batch.y) + l2_penalty(w, 1e-5)

opt.apply(tape.gradient(loss, w), clip_norm=1.0, lr=lr)

if loss < 1e-3: checkpoint(model, tag=f"epoch_{epoch:03d}")

sampler = TopK(k=40, temperature=0.8, repetition_penalty=1.1)

tokens = model.generate(prompt, max_len=256, stream=True)

yield from stream(tokens, on_token=lambda t: emit("delta", t))

suite = EvalSuite.load("humans_and_machines", split="holdout")

report = suite.run(model, seed=1337, workers=8, verbose=False)

assert report.pass_rate > 0.99, f"regression: {report.failures}"

embed = encoder(text, normalize=True, pooling="mean", dim=1536)

index.add(embed, meta={"src": "journal", "ts": now().isoformat()})

df = df.dropna().pipe(tokenize).pipe(dedupe).cache("corpus_v3")

wandb.log({"loss": loss, "step": step, "lr": sched.get_last_lr()})

model.push("a1brella/orakel-v2", private=True, card=MODEL_CARD)

probe = LinearProbe(layer=-2).fit(activations, labels, l1=1e-4)

attention = rollout(attn_maps, discard_ratio=0.9, head="mean")

dataset = interleave(synthetic(0.3), curated(0.7), seed=1337)

distill(teacher=orakel_xl, student=orakel_mini, kl_weight=0.5)

def train(model, corpus, epochs=64, lr=3e-4, warmup_steps=500):

for batch in loader(corpus, shuffle=True, batch_size=128):

loss = mse(model(batch.x), batch.y) + l2_penalty(w, 1e-5)

opt.apply(tape.gradient(loss, w), clip_norm=1.0, lr=lr)

POST /v1/agents/run -> 202 accepted (trace 8f3a..c21, 142ms)

queue depth 17 -> 4 | in-flight 12 | dead-letter 0 | lag 8ms

gpu[0] util 94% mem 61% temp 71c | gpu[1] util 88% mem 57%

retry backoff 250ms -> 1s -> settled after 2 attempts (node-4)

deploy canary 5% -> 25% -> 100% | error budget burn 0.02%/h

guardrail pii_filter: passed (0 redactions, 34.1k tokens scanned)

guardrail jailbreak_probe: passed | toxicity_gate: passed (p=0.01)

eval suite 312/312 green | latency p50 88ms p95 1210ms p99 2.4s

cache hit ratio 0.87 | embeddings served 1.2m/day | evictions 3%

tokens/s 1840 sustained | burst 4200 | throttle events (24h): 0

autoscale replicas 3 -> 5 (cpu 78%, target 60%, cooldown 300s)

heartbeat agent.planner ok | agent.critic ok | agent.router ok

snapshot memory.vec (1.2gb) saved to s3://a1brella-prod/snap/

rollout paused: human approval required (policy: two_person)

approval granted by operator@a1brella | audit id 44c1..9e02

rollout resumed -> 100% | uptime 99.98% (30d) | mttr 4m12s

drift detector: embedding shift 0.03 (threshold 0.10) nominal

billing: 41.2m tokens today | forecast 1.24b/mo | cap 2.00b/mo

incident #0 open | last page 27d ago | on-call rotation: nika

cron nightly_distill ok (38m) | cron eval_sweep ok (12m)

POST /v1/agents/run -> 202 accepted (trace 8f3a..c21, 142ms)

queue depth 17 -> 4 | in-flight 12 | dead-letter 0 | lag 8ms

gpu[0] util 94% mem 61% temp 71c | gpu[1] util 88% mem 57%

retry backoff 250ms -> 1s -> settled after 2 attempts (node-4)

deploy canary 5% -> 25% -> 100% | error budget burn 0.02%/h

guardrail pii_filter: passed (0 redactions, 34.1k tokens scanned)

guardrail jailbreak_probe: passed | toxicity_gate: passed (p=0.01)

eval suite 312/312 green | latency p50 88ms p95 1210ms p99 2.4s

cache hit ratio 0.87 | embeddings served 1.2m/day | evictions 3%

tokens/s 1840 sustained | burst 4200 | throttle events (24h): 0

autoscale replicas 3 -> 5 (cpu 78%, target 60%, cooldown 300s)

heartbeat agent.planner ok | agent.critic ok | agent.router ok

snapshot memory.vec (1.2gb) saved to s3://a1brella-prod/snap/

rollout paused: human approval required (policy: two_person)

approval granted by operator@a1brella | audit id 44c1..9e02

rollout resumed -> 100% | uptime 99.98% (30d) | mttr 4m12s

drift detector: embedding shift 0.03 (threshold 0.10) nominal

billing: 41.2m tokens today | forecast 1.24b/mo | cap 2.00b/mo

incident #0 open | last page 27d ago | on-call rotation: nika

cron nightly_distill ok (38m) | cron eval_sweep ok (12m)

POST /v1/agents/run -> 202 accepted (trace 8f3a..c21, 142ms)

queue depth 17 -> 4 | in-flight 12 | dead-letter 0 | lag 8ms

gpu[0] util 94% mem 61% temp 71c | gpu[1] util 88% mem 57%

retry backoff 250ms -> 1s -> settled after 2 attempts (node-4)

const studio = await a1brella.init({ region: "eu-west", seed: 42 })

const agent = studio.spawn({ role: "planner", model: "orakel-v2" })

agent.observe(worldState, { channels: ["sensor", "ops", "human"] })

const plan = agent.plan({ horizon: 12, budget: tokens(80_000) })

for (const step of plan.steps) await step.execute({ dryRun: false })

if (plan.confidence < 0.92) agent.replan({ strategy: "beam", k: 5 })

telemetry.trace("plan.settled", { id: plan.id, latencyMs: 142 })

const memory = new VectorStore({ dim: 1536, metric: "cosine" })

await memory.upsert(chunk.embedding, { ttl: "7d", src: "journal" })

const recall = await memory.query(intent, { k: 8, minScore: 0.72 })

ctx.inject(recall.map(r => r.text).join("\n"), { role: "system" })

export default pipeline(observe, plan, act, reflect, checkpoint)

const critic = studio.spawn({ role: "critic", temperature: 0.2 })

critic.review(plan).then(patch => merge(patch, { atomic: true }))

await handoff(agent, human, { reason: "edge_case", urgency: "low" })

studio.on("drift", metrics => recalibrate(metrics, { decay: 0.9 }))

const guard = compose(piiFilter, jailbreakProbe, toxicityGate)

router.route(intent, { fallback: human, timeoutMs: 30_000 })

await journal.append({ actor: agent.id, action: plan.summary })

scheduler.every("5m", () => agent.sync({ source: "worldState" }))

const studio = await a1brella.init({ region: "eu-west", seed: 42 })

const agent = studio.spawn({ role: "planner", model: "orakel-v2" })

agent.observe(worldState, { channels: ["sensor", "ops", "human"] })

const plan = agent.plan({ horizon: 12, budget: tokens(80_000) })

for (const step of plan.steps) await step.execute({ dryRun: false })

if (plan.confidence < 0.92) agent.replan({ strategy: "beam", k: 5 })

telemetry.trace("plan.settled", { id: plan.id, latencyMs: 142 })

const memory = new VectorStore({ dim: 1536, metric: "cosine" })

await memory.upsert(chunk.embedding, { ttl: "7d", src: "journal" })

const recall = await memory.query(intent, { k: 8, minScore: 0.72 })

ctx.inject(recall.map(r => r.text).join("\n"), { role: "system" })

export default pipeline(observe, plan, act, reflect, checkpoint)

const critic = studio.spawn({ role: "critic", temperature: 0.2 })

critic.review(plan).then(patch => merge(patch, { atomic: true }))

await handoff(agent, human, { reason: "edge_case", urgency: "low" })

studio.on("drift", metrics => recalibrate(metrics, { decay: 0.9 }))

const guard = compose(piiFilter, jailbreakProbe, toxicityGate)

router.route(intent, { fallback: human, timeoutMs: 30_000 })

await journal.append({ actor: agent.id, action: plan.summary })

scheduler.every("5m", () => agent.sync({ source: "worldState" }))

const studio = await a1brella.init({ region: "eu-west", seed: 42 })

const agent = studio.spawn({ role: "planner", model: "orakel-v2" })

agent.observe(worldState, { channels: ["sensor", "ops", "human"] })

const plan = agent.plan({ horizon: 12, budget: tokens(80_000) })

def train(model, corpus, epochs=64, lr=3e-4, warmup_steps=500):

for batch in loader(corpus, shuffle=True, batch_size=128):

loss = mse(model(batch.x), batch.y) + l2_penalty(w, 1e-5)

opt.apply(tape.gradient(loss, w), clip_norm=1.0, lr=lr)

if loss < 1e-3: checkpoint(model, tag=f"epoch_{epoch:03d}")

sampler = TopK(k=40, temperature=0.8, repetition_penalty=1.1)

tokens = model.generate(prompt, max_len=256, stream=True)

yield from stream(tokens, on_token=lambda t: emit("delta", t))

suite = EvalSuite.load("humans_and_machines", split="holdout")

report = suite.run(model, seed=1337, workers=8, verbose=False)

assert report.pass_rate > 0.99, f"regression: {report.failures}"

embed = encoder(text, normalize=True, pooling="mean", dim=1536)

index.add(embed, meta={"src": "journal", "ts": now().isoformat()})

df = df.dropna().pipe(tokenize).pipe(dedupe).cache("corpus_v3")

wandb.log({"loss": loss, "step": step, "lr": sched.get_last_lr()})

model.push("a1brella/orakel-v2", private=True, card=MODEL_CARD)

probe = LinearProbe(layer=-2).fit(activations, labels, l1=1e-4)

attention = rollout(attn_maps, discard_ratio=0.9, head="mean")

dataset = interleave(synthetic(0.3), curated(0.7), seed=1337)

distill(teacher=orakel_xl, student=orakel_mini, kl_weight=0.5)

def train(model, corpus, epochs=64, lr=3e-4, warmup_steps=500):

for batch in loader(corpus, shuffle=True, batch_size=128):

loss = mse(model(batch.x), batch.y) + l2_penalty(w, 1e-5)

opt.apply(tape.gradient(loss, w), clip_norm=1.0, lr=lr)

if loss < 1e-3: checkpoint(model, tag=f"epoch_{epoch:03d}")

sampler = TopK(k=40, temperature=0.8, repetition_penalty=1.1)

tokens = model.generate(prompt, max_len=256, stream=True)

yield from stream(tokens, on_token=lambda t: emit("delta", t))

suite = EvalSuite.load("humans_and_machines", split="holdout")

report = suite.run(model, seed=1337, workers=8, verbose=False)

assert report.pass_rate > 0.99, f"regression: {report.failures}"

embed = encoder(text, normalize=True, pooling="mean", dim=1536)

index.add(embed, meta={"src": "journal", "ts": now().isoformat()})

df = df.dropna().pipe(tokenize).pipe(dedupe).cache("corpus_v3")

wandb.log({"loss": loss, "step": step, "lr": sched.get_last_lr()})

model.push("a1brella/orakel-v2", private=True, card=MODEL_CARD)

probe = LinearProbe(layer=-2).fit(activations, labels, l1=1e-4)

attention = rollout(attn_maps, discard_ratio=0.9, head="mean")

dataset = interleave(synthetic(0.3), curated(0.7), seed=1337)

distill(teacher=orakel_xl, student=orakel_mini, kl_weight=0.5)

def train(model, corpus, epochs=64, lr=3e-4, warmup_steps=500):

for batch in loader(corpus, shuffle=True, batch_size=128):

loss = mse(model(batch.x), batch.y) + l2_penalty(w, 1e-5)

opt.apply(tape.gradient(loss, w), clip_norm=1.0, lr=lr)

POST /v1/agents/run -> 202 accepted (trace 8f3a..c21, 142ms)

queue depth 17 -> 4 | in-flight 12 | dead-letter 0 | lag 8ms

gpu[0] util 94% mem 61% temp 71c | gpu[1] util 88% mem 57%

retry backoff 250ms -> 1s -> settled after 2 attempts (node-4)

deploy canary 5% -> 25% -> 100% | error budget burn 0.02%/h

guardrail pii_filter: passed (0 redactions, 34.1k tokens scanned)

guardrail jailbreak_probe: passed | toxicity_gate: passed (p=0.01)

eval suite 312/312 green | latency p50 88ms p95 1210ms p99 2.4s

cache hit ratio 0.87 | embeddings served 1.2m/day | evictions 3%

tokens/s 1840 sustained | burst 4200 | throttle events (24h): 0

autoscale replicas 3 -> 5 (cpu 78%, target 60%, cooldown 300s)

heartbeat agent.planner ok | agent.critic ok | agent.router ok

snapshot memory.vec (1.2gb) saved to s3://a1brella-prod/snap/

rollout paused: human approval required (policy: two_person)

approval granted by operator@a1brella | audit id 44c1..9e02

rollout resumed -> 100% | uptime 99.98% (30d) | mttr 4m12s

drift detector: embedding shift 0.03 (threshold 0.10) nominal

billing: 41.2m tokens today | forecast 1.24b/mo | cap 2.00b/mo

incident #0 open | last page 27d ago | on-call rotation: nika

cron nightly_distill ok (38m) | cron eval_sweep ok (12m)

POST /v1/agents/run -> 202 accepted (trace 8f3a..c21, 142ms)

queue depth 17 -> 4 | in-flight 12 | dead-letter 0 | lag 8ms

gpu[0] util 94% mem 61% temp 71c | gpu[1] util 88% mem 57%

retry backoff 250ms -> 1s -> settled after 2 attempts (node-4)

deploy canary 5% -> 25% -> 100% | error budget burn 0.02%/h

guardrail pii_filter: passed (0 redactions, 34.1k tokens scanned)

guardrail jailbreak_probe: passed | toxicity_gate: passed (p=0.01)

eval suite 312/312 green | latency p50 88ms p95 1210ms p99 2.4s

cache hit ratio 0.87 | embeddings served 1.2m/day | evictions 3%

tokens/s 1840 sustained | burst 4200 | throttle events (24h): 0

autoscale replicas 3 -> 5 (cpu 78%, target 60%, cooldown 300s)

heartbeat agent.planner ok | agent.critic ok | agent.router ok

snapshot memory.vec (1.2gb) saved to s3://a1brella-prod/snap/

rollout paused: human approval required (policy: two_person)

approval granted by operator@a1brella | audit id 44c1..9e02

rollout resumed -> 100% | uptime 99.98% (30d) | mttr 4m12s

drift detector: embedding shift 0.03 (threshold 0.10) nominal

billing: 41.2m tokens today | forecast 1.24b/mo | cap 2.00b/mo

incident #0 open | last page 27d ago | on-call rotation: nika

cron nightly_distill ok (38m) | cron eval_sweep ok (12m)

POST /v1/agents/run -> 202 accepted (trace 8f3a..c21, 142ms)

queue depth 17 -> 4 | in-flight 12 | dead-letter 0 | lag 8ms

gpu[0] util 94% mem 61% temp 71c | gpu[1] util 88% mem 57%

retry backoff 250ms -> 1s -> settled after 2 attempts (node-4)

ON
DESIGNING
INTELLIGENT
SYSTEMS

Scadufax

AGENTIC DESIGN SYSTEMScadufax

Zappelin

AI PERSONAL ASSISTANTZappelin

Mono Synth

PRODUCTSMono Synth

SC Papers

RESEARCHSC Papers

Relay Ops

AUTOMATIONRelay Ops

Petal Vision

ON
WHO
WE
ARE

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About

A1BRELLA is an AI-driven creative studio building intelligent products, autonomous agents, and generative brand systems. We work where design meets machine intelligence — treating models, data, and interaction as raw creative material. Our approach combines strategy, design systems, and applied research to ship clear, scalable, human-centred AI experiences.