Most AI projects die at the strategy deck. We do the strategy and we ship the system. AI implementation consulting by engineers who actually implement.
Trusted by teams shipping production software
We do the strategy work — discovery, use-case selection, ROI modeling, build/buy decisions — and then we build the system. No handoff, no telephone game between strategy and engineering. Most engagements start with a 1–2 week discovery and end with a working production system in 8–12 weeks: strategy and shipping in the same room.
The deck nobody can ship.
You've got a backlog of ideas, a slide deck from a consultancy, maybe a pilot or two — but nothing actually running in production six months later.
Strategy by people who can't build.
The wrong use case, the wrong scope, off-the-shelf tools that don't fit. The handoff from strategy to a build vendor fell apart.
Pilots that never reach the stack.
A demo that works in isolation but never connects to your CRM, your data warehouse, or the systems your team works in every day.
Engagements scope to your stage. Some teams need help choosing the use case; others have the use case and need someone to build it.
- 01
Use-case discovery
We interview your team, map workflows, identify the highest-ROI use cases, and recommend what to ship first. Output: a written plan with cost, timeline, expected outcome.
- 02
Build/buy/integrate decisions
Sometimes the right answer is buying a tool. Sometimes it's integrating an API. Sometimes it's custom. We tell you which — and the reason, not the vendor relationship.
- 03
Model selection
Claude, GPT, Llama, Mistral, fine-tunes, embeddings. We pick based on the task — reasoning, throughput, cost, compliance — not based on hype.
- 04
RAG over your data
Retrieval-augmented generation against your docs, knowledge base, support history, or product data. Your team's answers come from your information.
- 05
Workflow automation
AI-driven automation that replaces manual work: classification, summarization, drafting, extraction, routing. Connected to the systems you already use.
- 06
Custom AI agents
When the workflow is multi-step and needs tool access, we build agents. (Deep-dive on /services/ai-agent-development.)
- 07
Integration with your stack
Salesforce, HubSpot, Slack, Jira, Notion, Zendesk, your internal tools, your data warehouse. AI that lives where your team works.
- 08
Evals + observability
We measure quality before launch and monitor it after. AI without evals is theater.
- 09
Training + rollout
Documentation, sessions for your team, gradual rollout, adoption metrics. Building the thing is half the work.
Discovery → architecture → MVP → harden → run. Same structure every time. Predictable timelines, fixed scope, no surprises.
Discovery
1–2 weeks. Use-case workshop, workflow mapping, ROI model. Output: ranked use cases with recommendation.
Architecture
1 week. Model selection, tool inventory, integration plan, eval criteria. Documented and reviewed with you.
MVP
2–4 weeks. Working system against real data. Your team starts using it immediately under controlled conditions.
Harden + ship
2–4 weeks. Edge cases, evals, monitoring, deployment, training. Production-ready.
Run
Ongoing. Model upgrades, prompt tuning, scope expansions. Optional but most clients keep us close.
We're model- and framework-agnostic but we have strong defaults. We pick based on the task — reasoning, throughput, cost, compliance — not the trend.
- ClaudeReasoning model
- GPT-4oReasoning model
- LlamaOpen-source LLM
- MistralOpen-source LLM
- EMEmbeddingsVector embeddings
- LangChainOrchestration
- LangGraphAgent graph
- Vercel AI SDKApp framework
- MCPMCPTool protocol
- pgvectorVector store
- PCPineconeVector store
- WVWeaviateVector store
- Postgres FTSFull-text search
- REST / GraphQLAPI layer
- WHWebhooksEvent triggers
- ZNZapier / n8nAutomation
- {}Custom APIsBespoke
- LangSmithTracing
- HeHeliconeLLM analytics
- LangfuseTracing
- AWSAWS BedrockManaged models
- AZAzure OpenAIManaged models
- OPOn-prem deploymentSelf-hosted
- Self-hosted modelsOpen weights
“Futur Labs shipped in six weeks what our internal team couldn't in eighteen months.”
Discovery
Use-case workshop, workflow mapping, ranked recommendations, ROI model. Standalone or rolled into a build engagement.
- Stakeholder interviews
- Workflow mapping
- Ranked use cases
- Build/buy/integrate recommendations
- Written plan with cost + timeline
Arlo
MCP-powered analytics agent: Claude queries 100+ marketing platforms in natural language. Our own product, running in production.
Agency ERP — AI ops layer
AI agents embedded in our own ERP for triage, scope estimation, and project status. Implementation of the patterns we ship to clients.
Annatype
Production AI product we built end-to-end — strategy, model selection, integrations, deployment.
Implementing AI means moving from idea to working software in production. That covers picking the right use case (most AI strategies fail at this step), choosing the right tools, building the system, integrating with your existing stack, and operating it once it's live. We do all of it.
Both — but in that order. We start with the strategy work (which use case, why now, what the success criteria are), then we build the thing ourselves. We don't write a 60-page deck and hand it to someone else. The people writing the strategy are the people writing the code.
Agent development is one outcome of AI implementation. Implementation is broader — it could mean an agent, a RAG system over your docs, a workflow automation, a custom internal tool, or a model fine-tune. The implementation engagement is about picking the right shape for your problem.
Yes — this is usually where we start. We run a 1–2 week discovery: interview your team, map workflows, identify the 2–3 highest-ROI use cases, and recommend which one to ship first. Output is a written plan with cost, timeline, and expected outcome.
Yes. Building the AI system is half the work — getting your team to use it is the other half. We do training, documentation, gradual rollout, and we stay on to fix the things that surface in real use.
We work in regulated environments. We can keep your data on your infrastructure, use models that contractually don't train on your data (Anthropic, Azure OpenAI, AWS Bedrock), or deploy open models on-prem. We've done HIPAA-adjacent and SOC 2 environments — we'll size up the constraints in discovery.
A few questions about the project so we come prepared — then we'll set up a short call to dig in.