SERVICES
Four disciplines, nineteen services, one job: put AI to work inside your business and keep it working. We develop the systems, engineer the LLM layer they run on, integrate them into your stack, and operate them for as long as you want us to.
Below is the full map - all nineteen AI development services, grouped by the discipline they belong to, each with a plain answer to the problem it solves. If you don’t yet know which one you need, the table further down is the fastest way to find out.
Four pillars, nineteen AI development services
The pillars are how we group the work, not how we invoice it. Most engagements draw on two of them, and nobody needs all four on day one. Every service below is a real page with its own scope, its own examples and its own guardrails.
AI Development
We build the system.
AI development is where the system itself gets built: the agents, the models behind them and the products they live inside. Start here when you know the job you want done and nothing has been built yet.
AI Agent Development
Purpose-built agents that own one job and do it properly.
We design, build and deploy autonomous agents that own a defined job end to end, with the tools, memory, guardrails and escalation path that job actually needs.
Custom AI & ML Development
Models and AI systems built for your data, not a generic API call.
Machine-learning systems trained on your own data for the problems a general-purpose model handles badly: forecasting, scoring, classification, extraction and ranking.
Generative AI Development
Products and features built on language, image and voice models.
Assistants, drafting tools, search, summarization and generation built into your own product, on top of language, image and voice models.
Software Development Agents
Reviews, tests, releases and dependency chores, handled.
Agents that live in your repository and pipeline, reviewing pull requests, writing tests, patching dependencies and shepherding releases while your engineers build.
AI Readiness Audit
Find the processes worth automating before spending a cent building.
A short, fixed-scope assessment that maps your workflows, data and systems, scores every candidate process on value, feasibility and risk, and names the first one worth automating.
LLM Engineering
We engineer the layer underneath.
LLM engineering is the layer beneath the product: retrieval, memory, tool design, fine-tuning, evaluation and model routing. Buy it when something already works in a demo and you need it to hold at volume, at a cost you can defend, in month six.
LLM Engineering
Context, tools, routing, guardrails and evals. The unglamorous work that decides whether it holds.
The engineering discipline beneath any serious LLM product: context and memory design, tool interfaces, model routing, guardrails, evaluation harnesses and cost control.
RAG & Knowledge Systems
Your documents, data and tribal knowledge, retrievable and cited.
Retrieval systems that let a model answer from your documents, tickets and databases with citations, correct permissions and an accuracy figure you can measure.
Fine-Tuning & Evaluation
Smaller, cheaper, faster models that still clear your quality bar - proven by evals, not vibes.
Fine-tuning, distillation and evaluation suites that get a smaller, cheaper, faster model over your quality bar, with the evals to prove it before anything switches over.
Conversational AI & Voice Agents
Assistants that hold context, take action and know when to hand over.
Chat and voice assistants that hold context across a conversation, take real actions in your systems, and hand over to a person at the point you choose.
AI Integration
We wire it into what you already run.
Integration is what turns an AI system into part of your operation: connecting it to the CRM, ERP, helpdesk, repositories and warehouse you already run, and keeping agents coordinated once there is more than one. Buy it when the intelligence exists but it can’t reach your systems, or your agents don’t behave like a team.
AI Integration & Implementation
Connect AI to the CRM, ERP, helpdesk, repo and warehouse you already run.
We connect AI to the systems you already run, including the authentication, rate limits, retries and error handling that nobody budgets for and everybody hits.
Agent Orchestration
The layer that makes many agents behave like one team.
The coordination layer that turns several agents into one team: task planning, handoffs, retries, shared state, long-running work and a single audit trail.
Data Engineering for AI
Pipelines, embeddings and permissions, so agents see the right data and nothing else.
Pipelines, embeddings, vector stores and permission models, so agents read the right data at the right freshness and never see what they shouldn’t.
Self-Healing Infrastructure
Agents that detect, diagnose and repair before you wake up.
Agents that watch your infrastructure, diagnose failures from logs and traces, apply known fixes, verify recovery, and escalate only what is genuinely new.
Autonomous Workforce
We put it to work, and we run it.
This pillar is the outcome the other three make possible: agents doing the work of skilled professionals, department by department, with someone keeping them healthy. Buy it when you want the output rather than the project.
Business Process Automation
End-to-end processes that run without a queue.
We take one process from intake to outcome, exceptions included, and rebuild it so it runs continuously instead of waiting for whoever opens the queue.
Marketing Agents
Research, briefs, drafts, ads and reporting on a daily cadence.
Agents that research, brief, draft, schedule, run ads and report every day, with a human editor approving anything published under your name.
Sales Agents
Enrichment, prep, follow-up and CRM hygiene, done the same way every time.
Agents that enrich every lead, prepare every meeting, follow up on time and keep the CRM clean, so your reps spend the week in conversations.
Customer Support Agents
Triage, answer, escalate - with the customer’s context already loaded.
Agents that triage every ticket, answer from your own documentation with citations, act inside a set limit, and escalate with the customer’s context already loaded.
Finance & Back-Office Agents
Matching, chasing, reconciling, filing. Auditable by default.
Agents that match invoices, chase approvals, reconcile accounts and file the paperwork, leaving an audit trail on every action they take.
Managed AI Workforce
We run the hive, watch the evals and keep it healthy. You get the output.
We operate the hive for you: monitoring, evals, model routing, repairs, cost control and new agents each quarter, reported every month.
START FROM THE PROBLEM
Which service do I need?
Pick the sentence that sounds most like your last leadership meeting. This table does the qualifying a first sales call would otherwise do, and it costs you nothing to read.
| What you’d say | Where to start | Why |
|---|---|---|
| “We don’t know where AI fits.” | AI Readiness Audit | Three to ten days of mapping and scoring, ending in a named first process. Cheaper than guessing. |
| “We have a process eating our week.” | Business Process Automation | One process, rebuilt end to end, exceptions included. |
| “Our chatbot gives wrong answers.” | RAG & Knowledge Systems | The problem is almost never the model. It’s retrieval, permissions and citation. |
| “Our AI demo won’t survive production.” | LLM Engineering | Evals, routing, guardrails and context design are what a demo skipped. |
| “It works, but it’s too slow and too expensive.” | Fine-Tuning & Evaluation | A smaller model, tuned and measured, usually clears the bar for a fraction of the cost. |
| “It can’t reach our systems.” | AI Integration & Implementation | The intelligence exists. The wiring doesn’t. |
| “We have agents, but they don’t coordinate.” | Agent Orchestration | Planning, handoffs, shared state and one audit trail across all of them. |
| “We built it, and nobody’s running it.” | Managed AI Workforce | Someone has to watch the evals, route the models and repair the drift. |
Still ambiguous? That’s normal, and it’s exactly what the audit is for.
HOW WE WORK
Every service runs on the same seven stages
Whichever pillar you start in, the shape of the engagement is the same. Timings are typical, and the audit tells you where yours will land.
-
Scout
Map the workflows, score the candidates.
-
Blueprint
Roles, boundaries, escalations and success metrics, agreed first.
-
Prototype
One agent, your real data, measured.
-
Forge
The production build and its integrations.
-
Guard
Evals, approval gates, audit logs, cost ceilings.
-
Release
Shadow, then assisted, then autonomous.
-
Tend
We watch it, improve it and add agents.
Frequently asked questions
What's the difference between AI development and LLM engineering?
AI development builds the thing a user touches: the agent, the model, the product around it. LLM engineering is the layer underneath - retrieval, context and memory design, tool interfaces, model routing, guardrails and evaluation - and it is what decides whether the thing still behaves at volume six months later. A demo needs the first. A production system needs both, and the second is where most pilots quietly fail.
Do we have to buy all four pillars?
No. Most clients start with an audit and one process, which usually touches two pillars at most. The pillars are an editorial grouping that makes nineteen services navigable, not a bundle. If a single service solves your problem, buy that one. We would rather deliver one working agent than sell you an architecture you don't need yet.
Where do we start if we've never automated anything?
With the AI Readiness Audit. It runs for three to ten days, maps your workflows, data and systems, and scores every candidate process on value, feasibility and risk. You finish with a scored backlog, a recommended first agent and an honest read on what isn't worth automating. It is fixed-scope and deliberately cheap, because its job is to stop you spending on the wrong process.
Can you work with an AI system we've already built?
Yes, and it's a common way to start. We assess what exists, measure it against evals rather than impressions, and tell you what to keep. Sometimes the fix is retrieval, sometimes it's orchestration, sometimes it's a smaller model and a tighter boundary. We will also tell you when a rebuild is cheaper than a rescue, which is not the answer most agencies give.
How do engagements work - project or retainer?
Three shapes. Scout is a fixed-fee audit that answers whether to automate anything at all. Forge is a fixed-scope build for one process area, blueprint through to controlled release. Hive is a monthly retainer where we operate what we built: evals, monitoring, model routing, repairs and new agents each quarter. Most clients run Scout, then Forge, then decide about Hive.