We design, build, and operationalise institutional‑grade software for trading, investment, and data‑intensive workflows.
Four core offers cover the full arc: prototype, integrate, architect, and advise. Pick the entry point that matches your urgency and internal capacity.
01. Rapid AI Prototyping & MVP Delivery
What it is
Weeks, not quarters: working software that proves the concept, validates stakeholders, and derisks budget decisions.
Use when
You have an idea, a mandate, or a gap—need to see it live before committing full spend.
Typical outputs
- Functional prototype (web/app/API) wired to sample or live data
- Technical blueprint: architecture, data model, risk & compliance notes
- Demo script and stakeholder pack
02. Data & System Integration / Workflow Automation
What it is
We connect FIX/REST/WebSocket feeds, internal services, and third‑party platforms into a governed, observable pipeline. Manual steps are removed, audit trails stay intact.
Use when
Teams are stuck in spreadsheets and email approvals. Latency, errors, or headcount are becoming bottlenecks.
Typical outputs
- Unified API layer + schema contracts
- ETL/ELT jobs, event streams, caching, monitoring
- Automated ops playbooks and runbooks
03. Platform Architecture & Scaling
What it is
Cloud‑native, resilient designs for low‑latency, high‑volume workloads. Built to evolve without forklift rebuilds.
Use when
The prototype works, but reliability, security, or scale will be questioned by risk, IT, or regulators.
Typical outputs
- Reference architecture (cloud/on‑prem/hybrid)
- Security, data governance, and deployment model
- Performance budgets, capacity plan, and cost envelope
04. Product Strategy & Advisory
What it is
Hands‑on product leadership: translating commercial goals into roadmaps, ROI models, and delivery plans that engineering and compliance can execute.
Use when
You need senior product bandwidth without adding headcount; or an external view to align stakeholders.
Typical outputs
- Prioritised roadmap, OKRs, pacing plan
- Regulatory posture and audit trail design
- Build/buy/partner decisions with cost scenarios
05. Operate / Transfer
What it is
We can run what we build or hand it over cleanly. Your choice.
Use when
You want continuity post‑launch or need to upskill an internal team to own the platform.
Typical outputs
- SRE/DevOps runbooks, dashboards, alerting
- Knowledge transfer workshops, documentation, pairing sessions
- Optional managed service SLA
How Engagements Run
- Discovery Call (30 min) – Fit check, problem framing, initial scope.
- Blueprint Sprint (≈2 weeks) – Prototype stub + plan. Fixed fee.
- Build & Iterate (2–6 months) – Agile delivery, fortnightly demos, automated tests.
- Operate / Transfer (ongoing or 4–8 weeks) – Run for you or hand it over.
Pricing & Commercials
- Fixed‑fee Blueprint, then milestone or retainer for build/operate
- Transparent burn rates and change control
- IP and code ownership structured to your needs
Ready to move?
FAQ
How do projects start?
30‑minute discovery call → fixed‑fee Blueprint sprint (~2 weeks) → build/iterate. No long-term commitment before the Blueprint.
What does the Blueprint sprint deliver?
A working prototype stub, target architecture, delivery plan, cost envelope, and risk/compliance notes. Enough for an investment decision.
How do you price builds?
Milestone-based or monthly retainer. Transparent burn rates. Change control on scope, never on surprises.
Who owns the IP and code?
You do. We assign all work-for-hire IP on receipt of payment. Third-party licenses (open source, APIs) are documented.
How big is the team?
A senior core (product + engineering) augmented with vetted specialists as needed. No junior body-shops, no bait-and-switch.
Do you use subcontractors or offshore teams?
Only when the skillset is specific and agreed up front. All contributors sign NDAs and follow the same security standards.
What tech stacks do you use?
Whatever fits the problem and your environment: Python/TypeScript/Java/C++, cloud-native (AWS/Azure/GCP), event-driven data, modern front-ends. We integrate with FIX, REST/WebSocket, Kafka, Databricks, etc.
How is “AI-native” applied in delivery?
LLMs accelerate coding, testing, doc generation, and data wrangling. We keep humans in the loop for design, security, and validation. No client IP is fed to external models without written approval.
Security and compliance?
We follow your infosec policies, isolate environments, and log access. Architecture includes authN/Z, audit trails, data governance, and regulatory considerations (e.g., MiFID II, MiCAR) where relevant.
Can you work within our Jira/Salesforce/Confluence stack?
Yes. We’ll adapt to your tooling or stand up a lightweight stack if you prefer.
Remote or on-site?
Primarily remote from London. On-site workshops as needed for discovery, architecture, and stakeholder alignment.
Typical timelines?
Blueprint: ~2 weeks. MVP: 4–8 weeks. Full platform: 3–6 months. Depends on scope, data access, and decision latency.
What happens after launch?
Option A: We run it (SLA/DevOps). Option B: We transfer with docs, runbooks, and training. Option C: Hybrid until your team is comfortable.
How do you measure success?
Defined at kickoff—time-to-first-value, manual hours removed, latency targets, adoption metrics. We report against agreed KPIs each sprint.
What if requirements change mid-project?
We re-baseline scope and budget via a lightweight change process. No hidden fees; trade-offs are explicit.
Do you take equity or rev-share?
Case-by-case. Cash-based projects are default; alternative structures considered if strategic.
Can you support regulated approvals or audits?
We prepare the technical artefacts (architecture docs, test evidence, logs). Formal legal/regulatory filings stay with your counsel/compliance.