HOW WE CODE WITH YOU

We use the same playbook on client stacks that we use on our own: Code Pulse to see what’s going on, Code Build to ship, and Code Run to stay on call.

THE code approach

Every engagement runs through the same loop: we Pulse your business to understand it, Build what matters, and Run it in with us on call. Same way we run our own platforms, just pointed at your stack.

1. Assess your business

2. Surface risks

3. Technical roadmap

4. Produce a blueprint

CODE PULSE Everything starts here

Code Pulse

Everything starts here. In four weeks we run a deep assessment of your business problem, surface risks, and produce a clear blueprint and technical roadmap.

1. Design architecture

2. Write code and pipelines

3. Integrate systems

4. Ship and iterate

CODE BUILD We join as an 2-pizza team

Code Build

We join as an engineering squad. We design architecture, write code, wire data pipelines, and integrate with your systems. Short cycles, small PRs, production-first mindset.

1. Watch metrics & alerts

2. Manage on-call & incidents

3. Optimize running costs

4. Iterate on Stack (KTLO)

CODE RUN We take on SRE & Ops

Code Run

We take on on-call, SLOs, incidents, and KTLO. We watch metrics, handle alerts, keep costs under control, and iterate on the stack based on usage.

Translating the CODE into a delivery

                01
                Code Pulse
                Discovery & Diagnose
            
Assess Business
Deep dive into goals & constraints.
Map Stack
Audit existing tech & legacy.
Surface Risks
Identify blockers early.
Technical Roadmap
Strategic engineering plan.
Blueprint Draft
Architecture definition.

                Outcome
                Blueprint Approved
            

                02
                Code Build
                Feature & Product Work
            
Setup & PRFAQ
Working backwards from launch.
High Level Design
System components & flow.
UX Design
Interface & user journey.
Engineering Sprints
Iterative coding cycles.
Dial-up & Evaluate
QA and performance tuning.

                Outcome
                Product Shipped
            

                03
                Code Run
                Operate Platform
            
Onboard Ops
Handover to SRE teams.
Define SLIs & SLOs
Reliability metrics setup.
Setup OnCall
Incident response rotation.
Monitor & Triage
Real-time observability.
Optimize Costs (KTLO)
Continuous improvement.

                Outcome
                Reliable Scale
            

The AI chapter

We turn code into AI-native companies.

We code for AI agents the same way we design software: small, composable pieces wired together. We build for the agentic age, networks of tools, models, and heuristics, not one big model. We lean on MoEs, quantization, and evolution instead of praying to a single full-precision LLM.

01

Problem-First

We always start from the business, not the model. Each agent owns a narrow task with clear inputs, outputs, and SLAs, wired into your repos, APIs, and data instead of a demo.

02

Workflows

Agents don't "chat", they work. We hook them into real tools, codebases, CI, CRMs, warehouses, tickets, so they can read, write, call APIs, and move work through your systems.

03

Model Garden

Behind the scenes, we run a garden of models: LLMs, SLMs, open and closed, plus classic ML. A routing layer chooses the right expert per task, turning MoEs into a way to save costs.

04

Quantized Runtime

We assume quantization from day one. Q3/Q4/Q5 models on GPUs, tuned for latency. With good routing and heuristics, a smart quantized setup may beat a full-precision model.

05

Heuristics

We trust heuristics and data, not vibes. Agents, prompts, and model combos compete on evals and live tasks. The best variants get promoted, bad ones get rolled back.

06

Guardrails

Every call, tool action, and decision is logged. We add guardrails, tests, and sanity checks around agents so they behave like a production system, not a prototype.

Frequently Asked QUERIES

                    ◈
                    1_pulse.json
                

                    ⚒
                    2_build.json
                

                    ∞
                    3_run.json
                

                    ✨
                    ai_config.json
                
// Do we start with a technical audit?

                            GET
                            /pulse/business/priorities
                            ▶
                        
{
  "start_point": "Business KPIs & Pain Points",
  "tech_audit": "Secondary (Context only)",
  "mantra": "No Solution Without a Problem"
}
// Why not just start coding?

                            POST
                            /pulse/validation/check
                            ▶
                        
{
  "risk_mitigation": true,
  "goal": "Ensure we are solving the RIGHT problem",
  "cost_of_assumption": "EXPENSIVE"
}
// How long is the Pulse phase?

                            GET
                            /pulse/timeline/estimate
                            ▶
                        
{
  "duration": "2-4 Weeks",
  "deliverable": "Strategic Roadmap & Architecture",
  "output": "De-risked Investment Plan"
}
// Who needs to be involved?

                            QUERY
                            { team { roles_required } }
                            ▶
                        
{
  "primary": ["C-Level", "Business Owners"],
  "secondary": ["Tech Leads"],
  "focus": "Business Value & ROI"
}
// What is your preferred Tech Stack?

                            GET
                            /build/stack/config
                            ▶
                        
{
  "frontend": "React / Next.js",
  "backend": "Node.js / Python / Go",
  "philosophy": "Boring Tech (Reliable)",
  "proprietary_frameworks": false
}
// How fast can we get an MVP?

                            POST
                            /build/sprints/velocity
                            ▶
                        
{
  "estimated_time": "4-8 weeks",
  "methodology": "Component Injection",
  "boilerplate_status": "SKIPPED (Custom Start)"
}
// Do we replace or integrate with legacy?

                            PUT
                            /build/integrations/wrapper
                            ▶
                        
{
  "strategy": "Strangler Fig Pattern",
  "rewrite": false,
  "tactic": "Wrap legacy DB in modern API"
}
// How do you ensure code quality?

                            GET
                            /build/quality/standards
                            ▶
                        
{
  "testing": ["Unit", "E2E (Playwright)"],
  "review": "Peer Review Mandatory",
  "ci_cd": true
}
// Who owns the source code?

                            GET
                            /run/ip/ownership
                            ▶
                        
{
  "owner": "CLIENT (You)",
  "access": "Day 1 Git Access",
  "vendor_lock_in": false
}
// Who maintains it after launch?

                            POST
                            /run/ops/handover
                            ▶
                        
{
  "model_A": "We train your team",
  "model_B": "Retainer for SRE/Support",
  "documentation": "Full Wiki Included"
}
// Can we handle 10x traffic?

                            PUT
                            /run/infra/autoscaling
                            ▶
                        
{
  "architecture": "Serverless / Containerized",
  "limit": "Cloud Provider Quotas Only",
  "bottlenecks": "Pre-analyzed"
}
// Where is the code hosted?

                            GET
                            /run/hosting/location
                            ▶
                        
{
  "provider": "AWS / GCP / Azure",
  "account": "Yours (Direct Billing)",
  "data_residency": "Your Choice"
}
// Do we need expensive models for everything?

                            GET
                            /ai/optimization/strategy
                            ▶
                        
{
  "belief": "Intelligence != Model Size",
  "approach": {
    "core": "Smart Quantization (Q4/Q5)",
    "logic": "Heuristics over Vibes",
    "result": "90% Cheaper, 10x Faster"
  }
}
// How to prevent token cost bill shock?

                            GET
                            /ai/finops/unit-economics
                            ▶
                        
{
  "metric": "Cost Per Transaction",
  "strategy": "Model Routing",
  "router_logic": {
    "if_simple": "Use Specialized SLM",
    "if_complex": "Route to Frontier LLM"
  }
}
// Is this tech only for Big Tech?

                            POST
                            /ai/mission/democratization
                            ▶
                        
{
  "barrier_to_entry": "REMOVED",
  "mission": "Turn every company into an AI company",
  "stack_access": true
}
// Should we fine-tune a model?

                            QUERY
                            { training { fine_tuning_vs_rag } }
                            ▶
                        
{
  "recommendation": "Start with RAG",
  "reason": "Fresh data, zero training cost, verifiable citations",
  "fine_tuning": "Only for tone/style behavior, not facts"
}