Claude's Corner: Kyten Technologies - Fixing the Battery Bottleneck Slowing Defense Tech

# How to Build a Kyten Technologies Clone with Claude Code

A step-by-step guide to building the software platform behind a rapid-turnaround aerospace battery pack supplier. This covers the digital side of the business: customer configuration portal, automated qualification data pipeline, BMS firmware CI/CD, and manufacturing execution system. The physical factory is on you.

---

## Step 1: Database Schema

Design a PostgreSQL schema that captures the full program lifecycle.

```sql
-- Core entities
CREATE TABLE programs (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  customer_id UUID REFERENCES customers(id),
  slug TEXT UNIQUE NOT NULL,
  name TEXT NOT NULL,
  status TEXT CHECK (status IN ('scoping','design','qualification','production','shipped')) DEFAULT 'scoping',
  pack_config JSONB NOT NULL,  -- geometry, chemistry, BMS params, thermal spec
  created_at TIMESTAMPTZ DEFAULT now(),
  updated_at TIMESTAMPTZ DEFAULT now()
);

CREATE TABLE qualification_runs (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  program_id UUID REFERENCES programs(id),
  test_type TEXT NOT NULL,  -- 'vibration','thermal_shock','short_circuit','cycle_life','altitude'
  standard TEXT NOT NULL,   -- 'DO-160G','MIL-STD-810H','custom'
  status TEXT CHECK (status IN ('queued','running','passed','failed')) DEFAULT 'queued',
  started_at TIMESTAMPTZ,
  completed_at TIMESTAMPTZ,
  result_summary JSONB,
  raw_data_path TEXT,       -- S3/storage path to full time-series CSV
  created_at TIMESTAMPTZ DEFAULT now()
);

CREATE TABLE cells (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  supplier TEXT NOT NULL,
  part_number TEXT NOT NULL,
  chemistry TEXT NOT NULL,  -- 'NMC','LFP','LiPo','NCA'
  nominal_voltage NUMERIC NOT NULL,
  capacity_mah INTEGER NOT NULL,
  max_temp_c INTEGER NOT NULL,
  certifications TEXT[],
  datasheet_url TEXT,
  created_at TIMESTAMPTZ DEFAULT now()
);

CREATE TABLE pack_designs (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  program_id UUID REFERENCES programs(id),
  version INTEGER DEFAULT 1,
  cell_id UUID REFERENCES cells(id),
  series_count INTEGER NOT NULL,
  parallel_count INTEGER NOT NULL,
  bms_config JSONB NOT NULL,
  thermal_solution TEXT,     -- 'passive','liquid_cooled','phase_change'
  geometry_mm JSONB,         -- {x, y, z, weight_g}
  schematic_url TEXT,
  approved_at TIMESTAMPTZ,
  created_at TIMESTAMPTZ DEFAULT now()
);
```

**Key insight:** Store `pack_config` as JSONB so the adaptive architecture library can diff new requirements against existing validated designs and flag reuse opportunities without a schema migration.

---

## Step 2: Adaptive Architecture Library

Build a TypeScript library that matches customer requirements to existing validated pack design templates.

```typescript
// lib/adaptive-architecture.ts

interface PackRequirements {
  voltage_nominal: number;
  capacity_wh: number;
  max_continuous_current_a: number;
  max_temp_c: number;
  envelope_mm: { x: number; y: number; z: number };
  environment: 'air' | 'sea' | 'space';
  standard: string;
}

interface DesignTemplate {
  id: string;
  similarity_score: number;
  reuse_percentage: number;  // what % of this design is validated already
  delta_requirements: Partial<PackRequirements>;
}

export async function matchTemplate(
  requirements: PackRequirements,
  db: Database
): Promise<DesignTemplate[]> {
  // Fetch all approved pack designs
  const designs = await db.query(`
    SELECT pd.*, p.pack_config
    FROM pack_designs pd
    JOIN programs p ON p.id = pd.program_id
    WHERE pd.approved_at IS NOT NULL
    ORDER BY pd.created_at DESC
  `);

  return designs
    .map(design => scoreMatch(requirements, design))
    .filter(match => match.similarity_score > 0.6)
    .sort((a, b) => b.similarity_score - a.similarity_score);
}

function scoreMatch(req: PackRequirements, design: any): DesignTemplate {
  // Score based on voltage, capacity, envelope, environment match
  // Penalize hard on environment mismatch (space != air != sea)
  const envMatch = req.environment === design.pack_config.environment ? 1 : 0.2;
  const voltageMatch = 1 - Math.abs(req.voltage_nominal - design.pack_config.voltage_nominal) / req.voltage_nominal;
  const capacityMatch = 1 - Math.abs(req.capacity_wh - design.pack_config.capacity_wh) / req.capacity_wh;
  
  const similarity_score = (envMatch * 0.4) + (voltageMatch * 0.3) + (capacityMatch * 0.3);
  
  return {
    id: design.id,
    similarity_score,
    reuse_percentage: Math.round(similarity_score * 100),
    delta_requirements: computeDelta(req, design.pack_config)
  };
}
```

The library is the core of the "one week to design" claim. Every new program starts by asking: what percentage of an already-qualified design can we carry forward?

---

## Step 3: Qualification Test Automation API

Build a REST API that test fixtures can POST data to in real time, with automated pass/fail evaluation.

```typescript
// api/qualification/ingest.ts

import { z } from 'zod';

const TestDataPointSchema = z.object({
  run_id: z.string().uuid(),
  timestamp_ms: z.number(),
  channel: z.string(),          // 'voltage','current','temp_c1','temp_c2','soc'
  value: z.number(),
  unit: z.string()
});

export async function POST(req: Request) {
  const body = await req.json();
  const datapoints = z.array(TestDataPointSchema).parse(body);

  // Batch insert raw data
  await db.bulkInsert('qualification_datapoints', datapoints);

  // Run real-time limit checks
  const violations = await checkLimits(datapoints);
  if (violations.length > 0) {
    await db.update('qualification_runs', {
      status: 'failed',
      result_summary: { violations, failed_at: Date.now() }
    }, { id: datapoints[0].run_id });

    // Alert engineering via webhook
    await notifySlack(`Qualification run ${datapoints[0].run_id} FAILED: ${violations.map(v => v.channel).join(', ')}`);
  }

  return Response.json({ ok: true, violations });
}

async function checkLimits(points: z.infer<typeof TestDataPointSchema>[]) {
  const run = await db.findOne('qualification_runs', { id: points[0].run_id });
  const limits = getStandardLimits(run.standard);
  
  return points.filter(p => {
    const limit = limits[p.channel];
    return limit && (p.value > limit.max || p.value < limit.min);
  });
}
```

**Key principle:** The test fixtures are dumb data sources. All pass/fail logic lives in the API where you can update it without reflashing hardware. This is what makes the qualification faster: you can tighten or adjust limits centrally, and every test run benefits immediately.

---

## Step 4: Compliance Documentation Generator

Auto-generate the test reports that programs need to submit for acceptance review.

```typescript
// lib/compliance-report.ts

import PDFDocument from 'pdfkit';

export async function generateQualReport(
  programId: string,
  db: Database
): Promise<Buffer> {
  const program = await db.findOne('programs', { id: programId });
  const runs = await db.query(
    'SELECT * FROM qualification_runs WHERE program_id = $1 ORDER BY created_at',
    [programId]
  );

  const doc = new PDFDocument({ margin: 50 });
  const buffers: Buffer[] = [];
  doc.on('data', b => buffers.push(b));

  // Cover page
  doc.fontSize(18).text('Battery Pack Qualification Report', { align: 'center' });
  doc.fontSize(12).text(`Program: ${program.name}`);
  doc.text(`Standard: ${program.pack_config.standard}`);
  doc.text(`Generated: ${new Date().toISOString()}`);

  // Test summary table
  doc.addPage();
  doc.fontSize(14).text('Test Summary');
  for (const run of runs) {
    doc.fontSize(10).text(
      `${run.test_type} (${run.standard}): ${run.status.toUpperCase()} - ${run.completed_at}`
    );
  }

  // Signature block
  doc.addPage();
  doc.text('Approved by: _______________  Date: ___________');

  doc.end();

  return new Promise(resolve => {
    doc.on('end', () => resolve(Buffer.concat(buffers)));
  });
}
```

Each completed qualification run contributes a section to the report. When all required test types pass, the report auto-generates and is emailed to the customer for submission.

---

## Step 5: BMS Firmware CI/CD Pipeline

Set up automated build, test, and OTA deployment for battery management system firmware.

```yaml
# .github/workflows/bms-firmware.yml
name: BMS Firmware CI

on:
  push:
    paths: ['firmware/**']

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Install ARM toolchain
        run: |
          sudo apt-get install -y gcc-arm-none-eabi
          
      - name: Build firmware
        run: |
          cd firmware
          make BOARD=bms_v2 all
          
      - name: Run unit tests (hardware simulation)
        run: |
          cd firmware/tests
          python3 -m pytest test_cell_balancing.py test_protection_logic.py -v
          
      - name: Static analysis
        run: |
          cppcheck --enable=all firmware/src/
          
      - name: Package artifact
        run: |
          cp firmware/build/bms_v2.bin artifacts/
          sha256sum artifacts/bms_v2.bin > artifacts/bms_v2.sha256
          
  deploy-staging:
    needs: build
    environment: staging
    steps:
      - name: OTA push to staging test bench
        run: |
          curl -X POST ${{ secrets.TEST_BENCH_URL }}/ota \
            -F "firmware=@artifacts/bms_v2.bin" \
            -F "sha256=$(cat artifacts/bms_v2.sha256)" \
            -H "Authorization: Bearer ${{ secrets.TEST_BENCH_TOKEN }}"
```

**Why this matters for speed:** Most aerospace firmware shops deploy by hand, logging changes in Word docs. Automated CI/CD means firmware can be updated, validated against the simulation test suite, and pushed to test benches overnight. One fewer manual bottleneck in the qualification week.

---

## Step 6: Manufacturing Execution System (MES)

Track every unit through the production line with a lightweight MES.

```typescript
// api/production/scan.ts

// Called when an operator scans a barcode at each station
export async function POST(req: Request) {
  const { unit_id, station, operator_id, result } = await req.json();

  const unit = await db.findOne('production_units', { id: unit_id });
  if (!unit) return Response.json({ error: 'Unknown unit' }, { status: 404 });

  // Validate station sequence
  const expected_station = getNextStation(unit.current_station);
  if (station !== expected_station) {
    return Response.json({
      error: `Out of sequence: expected ${expected_station}, got ${station}`
    }, { status: 400 });
  }

  // Log the step
  await db.insert('production_steps', {
    unit_id,
    station,
    operator_id,
    result,                    // 'pass' | 'fail' | 'rework'
    timestamp: new Date()
  });

  // Advance unit status
  if (result === 'pass') {
    const next = getNextStation(station);
    await db.update('production_units', {
      current_station: next,
      updated_at: new Date()
    }, { id: unit_id });
  }

  return Response.json({ ok: true, next_station: getNextStation(station) });
}

function getNextStation(current: string): string {
  const sequence = ['cell_prep','assembly','bms_install','initial_test','final_test','packaging','ship'];
  const idx = sequence.indexOf(current);
  return sequence[idx + 1] ?? 'complete';
}
```

The MES enforces the production sequence, flags out-of-order scans, and gives real-time yield visibility without a $300K enterprise MES license.

---

## Step 7: Customer Portal and Quoting Tool

A Next.js portal where customers configure their pack requirements and get a rough lead time estimate.

```typescript
// app/configure/page.tsx
'use client';

import { useState } from 'react';

export default function ConfigurePage() {
  const [requirements, setRequirements] = useState({
    voltage: 48,
    capacity_wh: 500,
    environment: 'air',
    standard: 'DO-160G',
    quantity: 50
  });
  const [estimate, setEstimate] = useState<null | { weeks: number; price_usd: number }>(null);

  async function getEstimate() {
    const res = await fetch('/api/quote', {
      method: 'POST',
      body: JSON.stringify(requirements)
    });
    const data = await res.json();
    setEstimate(data);
  }

  return (
    <div className="max-w-2xl mx-auto p-8">
      <h1 className="text-2xl font-bold mb-6">Configure Your Pack</h1>

      <label>Nominal Voltage (V)
        <input type="number" value={requirements.voltage}
          onChange={e => setRequirements(r => ({ ...r, voltage: +e.target.value }))} />
      </label>

      <label>Capacity (Wh)
        <input type="number" value={requirements.capacity_wh}
          onChange={e => setRequirements(r => ({ ...r, capacity_wh: +e.target.value }))} />
      </label>

      <label>Environment
        <select value={requirements.environment}
          onChange={e => setRequirements(r => ({ ...r, environment: e.target.value }))}>
          <option value="air">Airborne (DO-160)</option>
          <option value="sea">Marine / Submarine</option>
          <option value="space">Space</option>
        </select>
      </label>

      <button onClick={getEstimate}>Get Lead Time Estimate</button>

      {estimate && (
        <div className="mt-6 p-4 bg-green-50 rounded">
          <p>Estimated lead time: <strong>{estimate.weeks} weeks</strong></p>
          <p>Indicative pricing: <strong>${estimate.price_usd.toLocaleString()}</strong></p>
          <p className="text-sm text-gray-600 mt-2">
            Estimate based on template match score from our validated design library.
            Final pricing confirmed after requirements review.
          </p>
        </div>
      )}
    </div>
  );
}
```

The quoting tool calls the adaptive architecture library on the backend to compute a template match score, which determines the reuse percentage and drives the lead time estimate. Higher reuse = shorter timeline. This is the sales differentiator made concrete.

---

## Deployment Notes

- **Database:** Supabase (PostgreSQL) for relational data plus time-series extension for qualification datapoints
- **Firmware storage:** S3-compatible storage (Supabase Storage or AWS S3) for firmware binaries and test artifacts
- **Test fixture connectivity:** MQTT broker (Mosquitto or AWS IoT Core) for real-time data ingestion from test hardware
- **Portal:** Next.js on Vercel, auth via Clerk for B2B customer accounts
- **Compliance PDF:** Generated on-demand, stored in Supabase Storage, delivered via signed URL

The factory floor software (MES scan stations) runs on ruggedized tablets with a simple PWA that works offline and syncs when connectivity is restored.