GRDS // AI

AI WITHOUT THE VIBES.

GRDS // AI

We turn AI ambition into measurable, secure, cost-controlled implementation.

// 00 · What We Do

Boutique AI implementation for teams that value outcomes over hype.

Focus
End-to-end AI implementation — from feasibility assessment through production deployment and ongoing measurement.
Clients
Mid-market and enterprise teams in regulated industries (finance, healthcare, legal) where security and accuracy are non-negotiable.
Philosophy
Ship fast, measure everything, keep costs predictable. No black boxes, no magic — just disciplined engineering with AI.
Scale
Boutique by design. Every engagement is led by a senior practitioner who writes code and makes decisions — not a rotating cast of junior consultants.

// 01 · Approach

How we work: four phases, zero waste.

  1. Learn

    We embed with your team to understand the problem, the data, the constraints, and the definition of success. Output: a one-page spec with measurable targets, not a 40-slide deck.

  2. Design

    Architecture, model selection, cost modeling, and security review. We deliver a technical blueprint that maps directly to your infrastructure and compliance requirements.

  3. Implement

    Build, test, harden. We ship in tight cycles with clear checkpoints. Code is handoff-ready — your team owns it from day one, with thorough documentation and no proprietary lock-in.

  4. Measure

    If you can't measure it, you didn't build it. We instrument every output for accuracy, latency, cost, and business impact. Dashboards, alerts, and feedback loops — built-in, not bolted-on.

// 02 · Differentiators

What sets us apart in a crowded AI market.

Data Security

Regulatory-grade data handling by default. We architect for zero-trust, encrypted-at-rest, and minimal-data principles. SOC 2, HIPAA, and GDPR alignment from day zero — not as an afterthought.

Cost Discipline

LLM costs are predictable until they aren't. We model token economics before writing a single prompt, build cost guardrails into every pipeline, and provide real-time spend dashboards so there are no surprises.

Effective Outcomes

We define success in numbers, not vibes. Every engagement starts with measurable KPIs and ends with a scorecard. If it doesn't move the needle, we don't ship it.

// 03 · Services

Capabilities.

  • AI Readiness Assessment Feasibility analysis, data audit, cost projection, and risk assessment delivered in two weeks.
  • Custom AI Pipeline Development End-to-end pipelines: ingestion, preprocessing, model orchestration, output validation, and deployment. Built on your stack.
  • RAG & Knowledge Systems Retrieval-augmented generation over your proprietary data. Chunking strategies, embedding models, vector stores, and relevance tuning — all with measurable accuracy baselines.
  • Agentic Workflows Multi-step autonomous agents with human-in-the-loop checkpoints. Tool use, memory, planning, and guardrails for production reliability.
  • Model Fine-Tuning & Optimization Task-specific fine-tuning with rigorous evaluation harnesses. Quantization, distillation, and cost-optimized inference for your workload.
  • AI Security & Compliance Red-teaming, prompt injection defense, PII detection, audit logging, and compliance mapping for regulated environments.

// 04 · Engagement Model

How a project runs.

Phase Duration Deliverable Cost Model
Scoping & Proposal 1–2 weeks One-page spec + fixed-price estimate Free
Learn & Design 2–4 weeks Technical blueprint + cost model + risk register Fixed-price milestone
Implementation 4–12 weeks Working system + docs + tests + dashboards Fixed-price, milestone-billed
Measurement & Handoff 2–4 weeks Scorecard, runbook, team training Fixed-price milestone
Ongoing Support Optional retainer Monitoring, tuning, escalation support Monthly retainer

// 05 · FAQ

Common questions.

  • What makes this different from hiring a generalist dev shop?

    AI systems fail in specific, predictable ways — hallucination, prompt drift, cost overruns, adversarial inputs. We've spent years exclusively on AI implementation and know where the traps are. Generalist shops discover them on your dime.

  • Do you work with specific LLM providers or stay vendor-neutral?

    Vendor-neutral by design. We recommend the right model for the task — OpenAI, Anthropic, open-weight models, or a multi-provider routing layer — based on accuracy, cost, and latency requirements. No exclusive partnerships or kickbacks.

  • How do you handle data privacy?

    Data stays in your environment. We architect for zero data exfiltration, configure model providers for zero-retention inference where possible, and build PII detection and masking into every pipeline. We're comfortable working inside your VPC.

  • What does a typical engagement cost?

    Scoping and proposal are free. Learn & Design phases typically range from $15–35K depending on complexity. Full implementations range from $60–250K. We provide a fixed-price estimate after the scoping phase — no surprises, no scope creep without mutual agreement.

  • What if we already have an internal AI team?

    We accelerate them. Common patterns: we handle architecture and security design while your team focuses on feature work; we build the evaluation harness and cost guardrails your team didn't have time to create; or we parachute in to unblock a specific hard problem.

// 06 · Contact

Let's talk about what you're building.

We start every engagement with a no-cost scoping conversation. Tell us what you're trying to achieve, and we'll tell you if AI is the right answer — and what it should cost.

[email protected]