Deepvelopment

Full-stack engineering studio

Engineering that goesdeeper than the brief.

Eight disciplines in one delivery team: fintech, ERP, real-time 3D, commerce, growth, security, AI automation and smart contracts. We are finished when everything runs without us. No walls, no guesswork, nobody left in the dark.

Start a buildSee the 3D work
08
disciplines
200+
partners
07
years
60
fps budget
Double-Entry LedgersWebGL2 / WebGPULangGraph AgentsGLSL ShadersPSD2 / Open BankingRAG with CitationsNetSuiteHeadless CommerceTypeScript End to EndThreat ModellingCore Web Vitalspgvector / Hybrid SearchGPGPU ParticlesPCI-DSS Scope ReductionHuman-in-the-Loop GatesEvent-Driven IntegrationPenetration TestingEval Harnesses in CIServer-Side GTMglTF PipelinesPostgresSOC 2 ReadinessSmart Contract AuditsToken Vesting SchedulesCapital Raise SupportDouble-Entry LedgersWebGL2 / WebGPULangGraph AgentsGLSL ShadersPSD2 / Open BankingRAG with CitationsNetSuiteHeadless CommerceTypeScript End to EndThreat ModellingCore Web Vitalspgvector / Hybrid SearchGPGPU ParticlesPCI-DSS Scope ReductionHuman-in-the-Loop GatesEvent-Driven IntegrationPenetration TestingEval Harnesses in CIServer-Side GTMglTF PipelinesPostgresSOC 2 ReadinessSmart Contract AuditsToken Vesting SchedulesCapital Raise Support

What we build

Eight disciplines, one team.

-- / 08

Tap a discipline for the detail

A ledger that disagrees with the bank by a penny is an incident, not a bug. We build the money side of the product: books that balance by construction, payment flows that survive being retried, and reconciliation an auditor can follow without you in the room.

  • Immutable double-entry ledgers. Balances are derived from entries rather than stored and then updated.
  • Idempotency keys and replayable event logs, so a retry across a PSP boundary cannot charge someone twice.
  • Open Banking and PSD2 integrations, including the SCA and 3DS2 step-up flows nobody enjoys building.
  • KYC, KYB and AML screening with a decision trail you can hand to a regulator.
  • Reconciliation against PSP settlement files, run automatically, with break detection and ageing.
  • PCI-DSS scope kept small by tokenising at the edge, so card numbers never reach your own systems.
  • Stripe
  • Adyen
  • TrueLayer
  • Plaid
  • Postgres
  • Kafka
  • Temporal
  • Go
  • TypeScript
  • Decimal arithmetic

ERP projects rarely fail because somebody picked the wrong platform. They fail on data nobody validated and integrations nobody owned. We treat the migration and the integration work as the actual deliverable, which is what makes go-live the quiet part.

  • Discovery through to cutover: process mapping, gap analysis, and a chart of accounts that survives contact with finance.
  • Data migration with dry runs, row-level validation and a rollback we have already rehearsed.
  • Event-driven integration between ERP, storefront, WMS, 3PL and CRM, instead of a nightly CSV and hope.
  • Inventory across multiple warehouses and currencies, with landed cost modelled properly.
  • Custom modules where the platform runs out, written so the upgrade path stays open.
  • Hypercare, runbooks, and an operations team that can run the thing after we leave.
  • NetSuite
  • Dynamics 365 BC
  • Odoo
  • SAP B1
  • Celigo
  • Workato
  • REST/SOAP bridges
  • dbt
  • Airflow

Full-stack 3D, from authored geometry through to a render loop that holds 60fps on a mid-range laptop. We write shaders when the problem needs shaders, and when a frame costs 22ms we can tell you which pass is eating it.

  • GLSL and WGSL authoring: vertex displacement, PBR variants, SDF raymarching, custom lighting.
  • GPGPU on framebuffer ping-pong, which is how millions of particles and cloth get off the CPU.
  • Draw-call discipline. Instanced meshes, merged geometry, culling, LOD on screen-space error.
  • An asset pipeline that ships: glTF 2.0, Draco and meshopt, KTX2 transcoded per platform.
  • Deferred against forward+, cascaded shadow maps, TAA or FXAA when MSAA is unavailable.
  • Unreal Engine 5 with Epic Pixel Streaming when a scene will never fit a browser, and the judgement to say when WebGL is enough.
  • Budgets we say out loud: VRAM ceilings, texture memory, thermal throttling, WebGPU.
  • WebGL2
  • WebGPU
  • Three.js
  • R3F
  • GLSL
  • Unreal Engine 5
  • Pixel Streaming
  • Nanite / Lumen
  • Blender
  • KTX2

A storefront has two jobs. Load fast enough to rank, and tell the warehouse and finance the same story about what was sold. Plenty of builds manage one of those and quietly give up on the other.

  • Headless builds on Shopify with Hydrogen and Oxygen, or on Medusa and commercetools where that fits better.
  • Checkout performance work until LCP is under 1.2s on 4G, with no layout shift when the price or cart updates.
  • Subscriptions, bundles, pre-orders and tiered B2B pricing, built without a pile of plugins fighting each other.
  • Product data as a pipeline. PIM in one side, clean feeds out to Google, Meta, Amazon and affiliates.
  • Order lifecycle sync into ERP and 3PL, with idempotent webhooks and a dead-letter queue somebody actually reads.
  • Merchandising and CRO experiments measured on revenue per session rather than click-through.
  • Shopify
  • Hydrogen
  • Medusa
  • Next.js
  • Sanity
  • Algolia
  • Klaviyo
  • Stripe
  • Vercel

Technical SEO and paid media are engineering problems before they are marketing ones. Crawlable architecture, measurement that survives cookie loss, and budget judged against contribution margin rather than a dashboard full of impressions.

  • Technical SEO: crawl budget, log-file analysis, internal linking, and canonical and hreflang correctness.
  • Core Web Vitals treated as an SLO and tracked per template in the field, through CrUX and RUM.
  • Structured data at scale for Product, Offer, FAQ, Article and BreadcrumbList, validated in CI so it stays valid.
  • Programmatic and localised page systems that stay useful instead of turning thin six months later.
  • Server-side GTM, consent mode v2, and first-party measurement that still works when the cookies go.
  • Paid search, shopping, social and retargeting managed against blended ROAS and contribution margin.
  • GA4
  • BigQuery
  • Looker Studio
  • Server-side GTM
  • Screaming Frog
  • Ahrefs
  • Google Ads
  • Meta
  • Merchant Center

Security works when it is a property of the system rather than a report in a folder. Threat modelling, hardening and detection go into the delivery pipeline alongside everything else, which is the only way any of it survives a deadline.

  • STRIDE threat models per service, with abuse cases written next to the user stories.
  • SAST, DAST, dependency and secret scanning gated in CI, tuned well enough that nobody learns to ignore it.
  • Authenticated penetration testing, then remediation that gets retested rather than closed on trust.
  • Edge defence that holds during a launch: WAF rules, bot management, rate limits and a real DDoS posture.
  • Secrets, key rotation and least-privilege IAM, with no long-lived credentials sitting in a CI runner.
  • Detection and response: structured audit logs, alert thresholds that mean something, runbooks, tabletop exercises.
  • SOC 2 and ISO 27001 readiness, with evidence produced continuously instead of the week before the audit.
  • Burp Suite
  • Semgrep
  • Trivy
  • OWASP ASVS
  • Cloudflare
  • Vault
  • OIDC
  • Falco
  • Sentry

The demo is always easy. An agent left running against a real business is harder, because a wrong tool call moves money and a confident guess turns into a customer email. We build the parts that stop that happening.

  • LangGraph state machines in TypeScript, typed and checkpointed, so any run replays a step at a time.
  • Retrieval that cites its sources: hybrid BM25 and vector search, then a cross-encoder rerank.
  • Tool calls handled like payments. Idempotency keys, bounded retries, compensating actions.
  • A human gate in front of anything irreversible, with the diff on screen before it continues.
  • Work with no API behind it, driven as a real browser session at human pace.
  • Collection at scale from public sources, built to survive a layout change.
  • Token cost and p95 latency tracked per release, because an agent nobody costed gets switched off.
  • TypeScript
  • LangGraph
  • pgvector
  • Postgres
  • Temporal
  • Playwright
  • Anthropic
  • OpenAI
  • OpenTelemetry

The team has been in crypto for the best part of a decade, raising, building, launching, maintaining and auditing token economies. On-chain, a mistake is instant, public and irreversible, so the contracts get audited properly and the mechanics have to survive a real market.

  • Solidity and Anchor development, with the invariants written down before the code is.
  • Audits that combine manual review with fuzzing and symbolic execution, and a report you can publish.
  • Vesting and release schedules enforced on-chain rather than promised in a deck.
  • Token ecosystem design: supply, emissions, sinks, and what happens when incentives stop.
  • Raise support end to end: data room, diligence, launch strategy and the answers investors ask.
  • Treasury, multisig and timelock setup, with a key-loss story that is not a group chat.
  • Post-launch monitoring: mempool alerts, anomaly detection and a rehearsed incident path.
  • Solidity
  • Foundry
  • Anchor
  • Slither
  • Echidna
  • OpenZeppelin
  • Safe
  • Chainlink
  • viem

03 — deep dive

Inside the render loop.

Shipping real-time 3D means owning the whole chain, from authored geometry through compression, upload, shader programs and draw submission, inside the 16.6ms a frame gives you. The canvas below is live. Drag to orbit it, switch the debug view, and watch the counters move as you scroll.

0
triangles
0
draw calls
0
programs

live counters

PBR metal, roughness 0.18, IBL from a local env probe

  1. 01Vertex fetchInterleaved attribute buffers, index buffer, instance attributes
  2. 02Vertex shaderModel→view→clip transform, displacement, skinning, morph targets
  3. 03Primitive assemblyTriangle setup, backface cull, frustum + clip-space rejection
  4. 04RasterisationFragment generation, early-Z, quad overdraw — where fill rate dies
  5. 05Fragment shaderBRDF evaluation, texture sampling, shadow lookups, fog
  6. 06Output mergerDepth/stencil test, blending, MRT writes for a deferred pass
  7. 07Resolve & postMSAA resolve, tonemap, bloom, TAA history, colour space conversion

Frame time16.6 ms
Draw calls< 150
Triangles< 1.2 M
Texture memory< 256 MB
Shader compiles0 mid-scene
JS heap churn~0 per frame

// vertex — displacement on the GPU, curl noise sampled once per vertex
uniform float uTime;
uniform float uAmp;
varying vec3 vN;

void main() {
  vec3 p = position;
  float n = curl(p * 1.9 + uTime * 0.15).y;   // 3 taps, no derivative in VS
  p += normal * n * uAmp;

  // recompute the normal from the displaced neighbourhood, else lighting lies
  vec3 t = normalize(cross(normal, vec3(0.0, 1.0, 0.0)));
  vec3 pt = p + t * 0.01 + normal * curl((p + t * 0.01) * 1.9).y * uAmp;
  vN = normalize(cross(pt - p, cross(normal, t)));

  gl_Position = projectionMatrix * modelViewMatrix * vec4(p, 1.0);
}

On the asset side, geometry is authored in Blender, exported as glTF 2.0 and quantised with meshopt. Textures go out as KTX2 with Basis Universal, so one file serves ASTC to a phone and BC7 to a desktop. WebGPU is where this goes next: the same scene graph, WGSL in place of GLSL, and compute passes doing the work the framebuffer ping-pong does today.

Some scenes are never going to fit the budgets above. A configurator with film-grade materials, or an environment built on Nanite geometry, belongs in Unreal Engine 5 with Epic's Pixel Streaming. The frame renders on a GPU host, the browser receives video over WebRTC, and input travels back the other way, so fidelity stops being limited by whatever laptop opened the page.

What you trade is a download for a GPU instance per concurrent viewer, plus a latency floor set by how far the user sits from the nearest region. That is a real cost with a real ceiling on concurrency, which is why we will still argue for WebGL when WebGL is enough. Knowing which of the two a project needs is most of the value here.

07 — deep dive

Do anything.Appear as anything.Automate anything.

That is the pitch everyone is making this year. Actually meaning it is the hard part. An agent left running against a real business needs replayable state, auditable retrieval, and a person standing in front of anything irreversible. That last one is what most demos skip. The trace here is the shape of a run we would put in front of a client.

Run trace

live

Ingest
Embed
Retrieve
Ground
Act
Gate

·run.start invoice-triage cp_0

+210msingest 42 docs · 318 chunks

+140msembed 318 vectors · cached 61%

+190msretrieve bm25 ∪ vec 40 → rerank 8

+120msground 8 sources · 3 cited

+260msact erp.postCredit ic_9f2a

·gate side effect → awaiting human

·gate approved by finance@client

·run.end 2.1s · £0.038 · tr_71c4

Illustrative trace. The stages and the shape are real, the figures are not from your data

  1. 01IngestIncremental crawls. Chunking follows the structure of the document rather than a character count, and every chunk keeps a record of where it came from.
  2. 02EmbedThe model and the number of dimensions get chosen against your corpus rather than a leaderboard, then pinned to a version so results stop moving.
  3. 03RetrieveBM25 and vector search together, then a cross-encoder rerank. We measure recall@k on labelled data before anything goes near production.
  4. 04GroundEvery claim carries a citation. With no source to point at, the graph loops back for more context instead of writing something plausible.
  5. 05ActTool calls get idempotency keys and bounded retries, plus a compensating action for the case where step three succeeded and step four did not.
  6. 06GateAnything with a side effect stops for a person. They see the diff, then approve, edit or reject it. The run suspends to the checkpointer while it waits.

Cost per runpriced
p95 latency< 4 s
Recall@10> 0.90
Answers without a citation0
Side effects without a gate0
Runs you can replay100%

// typed state, one checkpoint per node, every edge explicit
const graph = new StateGraph<RunState>({ channels })
  .addNode("retrieve", retrieve)        // hybrid search + rerank
  .addNode("ground", ground)            // refuses without a citation
  .addNode("act", act)                  // idempotent tool calls
  .addNode("gate", humanApproval)

  // no source? go back for more context, do not guess
  .addConditionalEdges("ground", (s) =>
    s.citations.length > 0 ? "act" : "retrieve")

  // anything irreversible stops for a person
  .addConditionalEdges("act", (s) =>
    s.pending.some(isSideEffect) ? "gate" : END)

  .compile({ checkpointer, interruptBefore: ["gate"] });

Typed channels mean a malformed state fails at the node boundary rather than three steps later, where it is much more annoying to debug. The human gate is interruptBefore: the run suspends to the checkpointer and picks up again on approval, so an agent sitting for a day waiting on finance costs nothing while it waits.

Looking to automate something human? Plenty of work has no API behind it. Someone signs into a portal, reads a PDF, copies six numbers into another system, and does that four hundred times a month. We automate it as a real browser session driven at human pace, with the judgement calls routed to a person and everything it touched written to a trace you can audit afterwards.

We also know the other side of it. Browser fingerprinting and anti-bot systems are close enough to our security work that we can tell you upfront which of three situations you are in: a workflow that automates cleanly, a workflow that should really be an API conversation with the vendor, or one to leave alone. That answer arrives before you spend anything, which is usually worth more than the automation.

Non-negotiables

60fps
render budget held on mid-range hardware
<1.2s
LCP target on 4G for commerce templates
0
agent answers shipped without a citation
8disciplines
sitting in one delivery team

Partners & clients

Shipped alongside.

Two hundred and counting. A fashion label, two car makers' NFT drops, a crypto exchange, a government department and a skate park have all had very different definitions of done, and most of the rest we cannot name.

Blvck ParisMcLaren NFTPorsche NFTNikeGDS GroupCrypto.comChainlinkIBC Group (Mario Nawfal)IGBXAustralian GovernmentPump.funThe Cooking GuildHabibizEgg Heads ClubDystoApezBlvck ParisMcLaren NFTPorsche NFTNikeGDS GroupCrypto.comChainlinkIBC Group (Mario Nawfal)IGBXAustralian GovernmentPump.funThe Cooking GuildHabibizEgg Heads ClubDystoApez
YYGForgotten 3thereal WorldsBlank StudiosLlamaverseBlacks ClubCrypto Club GlobalInitial TalentPrivate FnFsSouthbank CentreBaySixty6Maynards BassettsPG GroupArcadia MarketingWeb Three ConsultingKickz.euYYGForgotten 3thereal WorldsBlank StudiosLlamaverseBlacks ClubCrypto Club GlobalInitial TalentPrivate FnFsSouthbank CentreBaySixty6Maynards BassettsPG GroupArcadia MarketingWeb Three ConsultingKickz.eu

How it runs

Four stages, and none of them is a status call.

01

Interrogate

We pull the problem apart before anyone writes code. Constraints, data, failure modes, and the thing you have not said out loud yet. Most of the risk on a project is findable in this first week if you go looking for it.

02

Prove

A thin slice through the whole stack, with real data and a real integration behind it. Either it holds up, or we have learned something cheaply and early instead of expensively in month five.

03

Build

Weekly increments you could ship, kept behind flags until you want them. Tests where they earn their keep, observability from the first deploy, and no branch alive long enough to rot.

04

Hand over

Runbooks, architecture decision records, and a team trained to operate the thing. The end of an engagement should not feel like a cliff edge.

Start a build

Tell us what has to hold under load.

Start a build→

Tell us the constraint before the spec. A budget range and a deadline let us answer you honestly in the first reply. Or write to hello@deepvelopment.com.