Custom AI Workflow Automation

Turn the manual work slowing your team down into a custom AI system.

Built with code, wired into your real tools, owned by you

If your team is copying data between tools, searching old docs, routing leads by hand, or answering the same operational questions every week, Slake builds the AI workflow that removes that drag.

Start with a focused AI Workflow Blueprint. You get the workflow map, feasibility read, and fixed-price build path before committing to a full implementation.

RAG apps AI internal tools Workflow automations AI MVPs Custom dashboards
AI Workflow Blueprint: $500
[ WHAT_HAPPENS_NEXT ]
  • 01 Quick intake: Tell me where work is getting stuck.
  • 02 Workflow review: We map the best AI use case together.
  • 03 Build path: You receive a clear blueprint and quote.
Try the live engine
[ THE_BUYER_PATH ]
01 Share Workflow
02 Map Blueprint
03 Review Quote
04 Build System
[ ENGINE_LIVE: STRIPE_V3 ]
v3.0.42_STABLE
[14:22:01] > INITIALIZING_VECTOR_SEARCH...
[14:22:02] > ANALYZING_IDEMPOTENCY_KEYS...
[14:22:03] > SYNTHESIZING_ARCHITECTURE_SPEC...
"For multi-party payouts in Stripe Connect, use Account Tokens for KYC-less onboarding layers while maintaining split-fee logic in the Transfer API..."
[ TECHNICAL_PROOF: LIVE_SYSTEM_CAPABILITY ]

Stop imagining AI.
Start deploying systems.

We don't build "chatbots." We build high-throughput intelligence layers that reason across your live documentation and data corpus with provable precision and zero-guess integrity.

EXPLORE_THE_DEMO ()
[ COMMON_AI_WORKFLOWS ]

Where AI usually pays for itself first

The best first build is rarely a giant AI transformation. It is one recurring workflow where your team already knows the pain and the data already exists.

[ DOCUMENTS ]

Document lookup and review

Teams search PDFs, contracts, support docs, invoices, job files, or internal notes before they can answer basic operational questions.

The Fix: A grounded AI search layer that finds the right source, summarizes it, and cites where the answer came from.
[ LEADS ]

Lead routing and follow-up

Inbound leads sit in inboxes, forms, CRMs, or spreadsheets while someone manually qualifies, tags, assigns, and drafts next steps.

The Fix: An AI routing loop that classifies the lead, enriches context, updates the CRM, and triggers the right follow-up.
[ OPERATIONS ]

Data entry and internal reporting

People copy information between email, spreadsheets, billing tools, CRMs, and dashboards because the systems do not talk cleanly.

The Fix: A coded automation layer that extracts, validates, syncs, and reports the data with traceable failure handling.
Live_System_Active

RAG engine turns Stripe API docs into architecture in 8.2s.

ENTER_COMMAND_CENTER
[ FINANCIAL.AUDIT: PERFORMANCE.LOSS ]

Quantify the Information Tax.

Use the tactical calculator below to determine the precise annual EBITDA drag caused by manual retrieval and context fragmentation.

10 hrs

// MEASURED_PER_RELEVANT_HEAD

$55/hr

// INCLUDES_1.3X_LABOR_BURDEN

Burden Multiplier 1.3x
Effective Hourly Cost $72/hr
Operational Latency 520 hrs
EBITDA_DRAIN_TOTAL
-$37,180
INITIATE_RECOVERY_PROTOCOL ()
[ ARCH.SPEC: PRODUCTION.BACKPLANE ]

HNSW / IVFFlat Indexing

Architecture is ready for billion-vector scale. We implement disk-optimized HNSW indexes via pgvectorscale to preserve 10x retrieval speeds as data grows. No performance degradation on high-density corpora.

64-Core SSD RAID 10

Configuration path for mission-critical searching. Systems are prepared to saturate 64-core concurrency and NVMe RAID arrays for high-throughput enterprise environments.

Citus Table Sharding

Scalability support for institutional data corpora. Architecture is ready for horizontal Citus sharding across distributed nodes to handle 10TB+ of records and 4,000+ queries per second.

Faithfulness Trace

Implementation ready for RAGAS-level observability. We eliminate hallucinations via granular semantic verification. Every response is ground-truth verified before it reaches your team.

// SYSTEM_STATUS: SCALABILITY_ROADMAP_CONFIGURED

ENGINEERING STRATEGY: BUILT ON YOUR STACK, NOT OURS.

Vertex AI
OpenAI
Anthropic
Gemini
Azure OpenAI
Supabase
pgvector
Pinecone
LangChain
Slack
Vercel
Stripe
Clerk
FastAPI
HubSpot
n8n
Python
Docker
[PREPARATION_LAYER]

What You Need for the AI Workflow Blueprint

  • One Workflow: A concrete loop where people constantly hunt for information.
  • One Real Example: A live ticket, job, or deal you can pull up on screen.
  • Access: Ability to screen‑share the tools that hold that data (CRM, Drive, etc.).
  • Decision Maker: Someone on the call who can speak for budget and environment access.

How the AI Workflow Blueprint & Build Sprint Works

A simple path from messy manual work to a production AI system your team can actually use.

01

Step 1: Find the Workflow

We identify the one manual loop where AI can save real time: lead routing, document lookup, support triage, CRM cleanup, invoice work, or internal reporting.

02

Blueprint + Fixed Quote

You get a plain-English workflow map, feasibility read, technical plan, and fixed-price build quote within 72 hours.

[MATH_OF_EBITDA_RECOVERY]
ROI = (Cmanual × Erate) - (Cai + Coversight)
// C_MANUAL: Fully Burdened Labor Cost
// E_RATE: Documentation Error Rate (10-15% Baseline)
// C_OVERSIGHT: Human-in-the-loop Validation cost
03

Step 2: Deploy (Capital Sprint)

Build and deploy the full production system in 2 to 4 weeks. You own the code and data infrastructure.

The "Bold Experiment" Example

How we think: Structural changes, not just better colors.

The Proposal

"Stop passing permit PDFs around in email. Move to a live job record where permit requirements, status, and documents are all attached to the work order."

Why it works (Systems Thinking)

  • Data Integrity: One source of truth instead of conflicting versions in inboxes.
  • Speed: Crews see exactly what’s approved and what’s missing without asking ops.
  • Result: Fewer delays, less rework, measurable drop in Field Ops Information Tax.
[SYSTEM_MODES]

PRODUCTION AI YOU OWN

We deploy production-ready systems in your cloud with no vendor lock-in. Start with one workflow and a fixed-price blueprint.

[SYS_ID: 0x01_RAG]

Retrieval Systems

Target: Crews & Operators. Typical clients are mid-market operations teams with real revenue on the line, not experimental AI labs.

Focus on 'Instant Answers' and eliminating document hunting. Embed retrieval into existing tools so teams pull specs in seconds instead of searching PDFs.

THE_FIX: Cuts time-to-answer by 60-80% through context-aware document synthesis.
[SYS_ID: 0x02_SIGNAL]

Signal Systems

Target: Managers. Designed for leaders at technically literate SMB and mid-market firms who need enterprise-grade visibility without a 20-person data team.

Focus on 'Clean Signal' and aggregating CRM, Billing, and Ops. Turn fragmented data into a single autonomous briefing for management.

THE_FIX: Keeps leadership in exception-only mode, increasing span of control.
[SYS_ID: 0x03_LOOP]

Automation Loops

Target: Founders & Admins. Ideal for B2B leaders who need to reclaim senior staff time by engineering out the manual glue work slowing down scale.

Focus on 'Operational Velocity' and automating the contract-to-onboarding flow. Reclaim senior leadership's time by engineering out glue work.

THE_FIX: Moves senior staff off $20/hr work and into scaling decisions.

Why Replace Scripts with Custom AI Systems?

01
Asset Construction, Not Duct Tape.

I build permanent data pipelines and retrieval graphs that compound over time, replacing fragile no-code setups.

02
Fixed-Scope Deployments.

We start with a targeted $4,000 to $8,000 sprint to wire a production loop to your specific CRM and document systems. No open-ended billing.

03
Principal Engineering Level.

You work directly with an architect designing around system risk, controls, and deployment realities, not a junior developer testing generic AI prompts on your data.

[ 05_OPERATIONAL_SAFEGUARDS ]
01 // THE DATA QUESTION: "What if my data is a mess?"
The blueprint is a feasibility stress-test. If your data is clean, we define the first working AI workflow. If the dataset is fragmented, the outcome is a Data Sanitization Roadmap so you know precisely what to clean before moving to a full sprint.
02 // THE CAPEX LOGIC: "Why build custom vs. buying a seat-license?"
SaaS seat-licenses are recurring OpEx Utility Expenses. Slake builds Capital Assets. You pay for the engineering once, and you own the resulting IP as a depreciable asset on your balance sheet. This converts monthly per-seat leakage into permanent company equity.
03 // THE M&A AUDIT: "Does this code add value at exit?"
Our systems are built for Technical Portability. We use an open-stack architecture (Next.js, SQL, Zod) that any engineering firm can audit or maintain. During an acquisition, your Slake Backplane is viewed as internal IP, not a third-party liability.
04 // THE OPS FRICTION: "Is this just another tab to log into?"
We don't build "Another Portal." We build Intervention Layers that work inside your existing tools (Slack, CRM, Email). This minimizes staff training and maximizes adoption velocity because your team never has to leave their primary workflow.
05 // THE ZERO-LEAKAGE GUARANTEE: "Is my data training an LLM?"
Absolutely not. We utilize Enterprise-Tier API Contracts that legally exclude your data from being used for generic model training. Your information remains inside your VPC. We favor zero-persistence ingestion to ensure your IP stays yours.
06 // THE RECURRING COST: "What are the hidden software bills?"
We favor usage-based, portable stacks. You pay only for raw API usage and hosting (OpenAI, Pinecone, Netlify). For a typical mid-market team, this usage-based cost is a fraction of equivalent SaaS seat-pricing. We provide a precise usage forecast during the blueprint.
07 // THE BUILD INTEGRITY: "What happens if the system stops working?"
Every Sprint includes 30 days of Build Integrity Support. If a logic defect is identified in our original implementation during this window, we restore it at zero cost. This protects your investment while you evaluate the initial deployment.