01 / AI Deployment & Integration
The right
AI harness.
Properly wired.
Most businesses know they need AI. Very few have someone to go in, assess the landscape, choose the right model and tooling, and integrate it so it actually changes how work gets done — not just adds another chatbot to the stack.
What this is
AI that changes
how the work gets done —
not how it looks.
The AI landscape in 2025 and 2026 is genuinely different from what it was 18 months ago. Frontier models can now reason across long documents, write and execute code, browse the web, operate desktop software, and take multi-step actions autonomously. The bottleneck isn't the AI — it's knowing which harness to put it in for your specific context.
We come in as an implementation partner, not a vendor. We assess your current tools, workflows, and team structure, identify where AI can be structurally embedded rather than bolted on, choose the right model and harness for each job, and wire it in properly — with the right prompting, guardrails, and integrations in place.
This isn't about using ChatGPT for your emails. This is about AI that has access to your systems, your data, and your workflows and can act on them — with the right human oversight at the right points.
What "properly wired" means
- The model has access to the right context — your documents, CRM, databases — not just a prompt
- Guardrails and confidence thresholds are set so high-stakes outputs still get human review
- The integration is into the tools your team already uses, not a new tab they have to open
- Prompting and system instructions are engineered, not improvised
- You have visibility: logs, evals, drift monitoring built in from day one
- Your team knows how to maintain, audit, and update the system themselves
The harnesses we deploy
The right tool
for the right job.
OpenAI · Codex
Codex Agent
by OpenAI
A powerful software engineering agent that can read, write, and execute code across entire codebases. We deploy Codex to automate development workflows, run test suites, generate and review pull requests, and handle repetitive engineering tasks — allowing your dev team to focus on the work that actually requires human judgement.
Best deployed for
Engineering teams looking to accelerate throughput without scaling headcount.
Anthropic · Claude
Claude Cowork
by Anthropic
Claude Cowork puts a deeply capable AI assistant directly into your team's desktop environment — with access to files, emails, calendars, and the tools your team actually uses. We configure and deploy Cowork with the right context, instructions, and integrations so it operates as a genuine team member, not a lookup tool.
Best deployed for
Knowledge workers, ops teams, and leadership who process large volumes of information daily.
Emerging · OpenClaw
OpenClaw
Open ecosystem harness
OpenClaw is an open-framework agent harness designed for businesses that need AI to operate across multiple tools and APIs simultaneously. We deploy OpenClaw where workflows span more than one system and require an orchestration layer — coordinating actions across CRMs, databases, comms tools, and external APIs without a single proprietary vendor locking you in.
Best deployed for
Multi-system operations where vendor independence matters and data flows across many platforms.
Nous Research · Hermes
Hermes Agent
by Nous Research
Hermes is a fine-tuned model series purpose-built for structured function calling and agentic tasks. We deploy Hermes where reliability, speed, and cost at scale matter — particularly in high-volume automation pipelines where a frontier-class model is overkill but precision is non-negotiable. Often the right engine underneath a larger automation stack.
Best deployed for
High-volume pipelines requiring fast, reliable structured outputs at a cost that doesn't hurt.
Browser · Computer Use
Computer Use Agents
Anthropic / OpenAI
The latest generation of AI can operate a desktop or browser directly — clicking, typing, navigating — without requiring API access to the underlying system. We deploy computer-use agents for legacy software, web scraping, form submission workflows, and any process that has traditionally required a human sitting in front of a screen.
Best deployed for
Legacy systems without APIs, browser-based workflows, and any task a human currently does on a screen.
Custom · Fine-tuned Models
Custom & Fine-tuned
Domain-specific deployment
Sometimes the right answer is a model fine-tuned on your data, your terminology, and your domain. We scope, prepare, fine-tune, and deploy custom models where off-the-shelf reasoning isn't precise enough — particularly for classification, document extraction, or industry-specific language that general models get wrong.
Best deployed for
Specialised industries where general model outputs consistently miss domain-specific nuance.
How it works
A structured path
from assessment to
live deployment.
01
AI Audit
We spend time inside your business — understanding your tools, your team structure, where time gets wasted, and where a decision or action currently requires a human that doesn't need to. You receive a written assessment with specific recommendations. This is free and carries no obligation.
02
Harness Selection
We map your identified use cases to the right model and harness. Not every problem needs a frontier model. Some need a fine-tuned specialist. Some need an agent with tool access. Some need a computer-use setup. We make the right call for the right job and explain why in plain language.
03
Integration Design
We design how the AI connects to your existing systems — your CRM, your databases, your document stores, your comms tools. This includes the prompting architecture, context retrieval strategy, tool definitions, and the human-in-the-loop checkpoints. Everything is documented before a line of code is written.
04
Build & Deploy
We implement and deploy the integration. Most first deployments go live within 30–60 days of project start. More complex multi-system deployments run 60–90 days. Your team is involved throughout, and we train them on how the system works and how to maintain it.
05
Monitor & Refine
AI deployments need watching, especially in the first weeks. We monitor performance, catch edge cases, tune prompting and thresholds, and adjust the integration as your team's usage patterns emerge. You retain full ownership of everything built.
What changes
Before & after
a proper deployment.
Before
Team manually reviews every inbound enquiry and decides how to route it, taking 20–40 minutes per lead across the day.
After
A Hermes agent reads, classifies, and routes every enquiry the moment it arrives — with a confidence score and draft response queued for one-click send.
Before
Senior staff spend Friday afternoons compiling the week's data from 4 different systems into a board report that takes 3 hours.
After
A Claude-powered pipeline pulls, synthesises, and drafts the report every Friday at 8am. A human reviews and sends. Total time: 12 minutes.
Before
Developers spend 30–40% of their time writing boilerplate, tests, and documentation — work they know how to do but find low-value.
After
Codex handles boilerplate, test generation, and first-pass documentation continuously. Engineers focus on architecture, product decisions, and the work that requires genuine creativity.
Before
Your team can't access the knowledge locked in 5 years of internal documents, client notes, and SOPs — it's all in Google Drive, largely unsearchable.
After
Claude Cowork has read and indexed your entire knowledge base. Any team member can ask a question in plain English and get an accurate, cited answer in seconds.
Questions
Things people ask
before getting started.
Consumer tools are general-purpose and stateless — they have no knowledge of your business, your data, or your systems. What we deploy is structurally different: AI with access to your actual context (documents, CRM, databases), integrated into the tools your team already uses, with engineered prompting and guardrails. The output quality is categorically different because the input context is.
We assess the job to be done: what inputs are involved, what outputs are required, what the latency and cost constraints are, and what the tolerance for error looks like. A task that needs deep reasoning over a complex document gets a different model than a task that needs fast, structured classification at volume. We explain the decision clearly before we build anything.
Yes, that's the point. We integrate into what you have — HubSpot, Salesforce, Notion, Google Workspace, Microsoft 365, Slack, Xero, and custom systems. We don't require you to change platforms. If a legacy system has no API, computer-use agents can often handle it anyway.
We NDA before the audit. For sensitive deployments we use zero-data-retention enterprise API tiers, or deploy models on infrastructure you control entirely — your AWS, Azure, or on-prem. Healthcare, legal, and financial clients are welcome. We're explicit about data flows and document them in the integration spec.
First deployments typically run 30–60 days from project start. Complex multi-system integrations can run 60–90 days. We give you a documented timeline before we begin, and we hold to it.
Start with
a free audit.
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