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How we work

From the first call to a system your team runs, with nothing hidden

Six stages, each with a clear purpose, a clear ask of you and a clear deliverable. This is the same process whether the outcome is a voice agent, a knowledge assistant or a data platform.

The path at a glance

  1. 1Discovery call60 min · free
  2. 2Deep-dive & data audit1–2 weeks
  3. 3Architecture & stack selection1 week
  4. 4Pilot build4–8 weeks
  5. 5Production hardening & rollout2–6 weeks
  6. 6Operate & improveongoing
How we work

Each stage in detail

Select a stage to see what happens, what we ask of you and what you receive.

01

Step 01 · 60 min · free

Discovery call

A working session with a senior engineer, not a sales rep. Before the call we research your company, industry, tech stack and recent initiatives. On the call we map the problem, the workflow around it and the constraints, and we tell you honestly whether AI is the right tool before anyone spends money.

What happens

  • Opening (10 min): one question anchors everything: which area of the business costs you the most time, effort or money, and what metric would prove it improved?
  • Process mapping (30 min): we walk the workflow end to end: who does what, in which systems, at what volume, with what turnaround and exception rate.
  • Opportunity surfacing (20 min): we map the data (where it lives, who owns it, how clean and fresh it is, whether it contains personal or regulated data) and sketch two or three candidate approaches with rough effort.
  • Constraints and next steps (10 min): cloud and data-residency rules, security review, compliance, budget band, deadline, and whether the next step is a deep-dive sprint, a scoped pilot or an honest “not yet”.

What we ask of you

  • The workflow as it runs today, including the messy exceptions
  • Monthly volumes, current cost or time per item, and service-level targets
  • Systems involved (CRM, ERP, helpdesk, warehouse, phone, WhatsApp) and who administers them
  • Who decides, who will own the system after launch, and the timeline you are working to
  • Anything you have already tried, and why it stalled

What you get

  • A written summary within 48 hours
  • A feasibility view: green, amber or red, with the reasons
  • A suggested pilot scope and an indicative budget band
  • A short intake list of what we would need to see next
How we choose

The decision matrix behind every architecture recommendation

Six questions decide most of the design. We answer them with your data during the deep-dive, and we write down why the alternatives lost.

QuestionIf yesIf no
Are the steps known in advance?LLM workflowAgent with tools
Must answers cite your documents?Hybrid RAG with rerankingDirect model call with structured output
Does the task span many systems or skills?Supervisor multi-agent with checkpointsSingle agent
Is the interface a phone call?Voice agent, cascaded or speech-to-speechChat, WhatsApp or in-app copilot
Is the task narrow, high-volume and plateaued on prompting?Fine-tune a small modelPrompt a frontier model with routing
Does it need an answer in seconds?Real-time path with caching and streamingBatch pipeline at half the price

The targets every system is designed and monitored against

<2sp95 response for chat and copilot answers, including retrieval
<800msMedian voice turn latency, p95 under 1.5 s, across telephony, speech and reasoning
99.9%Availability target with provider fallbacks and graceful degradation
100%Of model calls traced, costed and covered by an evaluation set
Speed & cost

What we engineer in for speed and cost

Speed and cost are architecture decisions, not vendor promises. Tap a lever to see how it works and the effect size the platforms publish.

Prompt caching

90% off cached input

System prompts, tool schemas and document context are cached at the provider. Anthropic bills cache reads at a tenth of the base input price, OpenAI discounts cached input by 90% on current models, and Gemini charges a fraction for cached context, so long prefixes stop costing full price on every call.
Source: Anthropic pricing
Engagement models

Choose the model that fits your vision

Fixed scope when the outcome is clear, a dedicated team when the roadmap is alive, or senior engineers inside your own squad.

Fixed-scope pilot

A defined outcome, timeline and price after the deep-dive sprint. Best for a first system.

  • Scoped from the audit and architecture doc
  • Weekly demos and releases
  • Fixed price with change control

Dedicated AI team

A cross-functional squad embedded with your product team for a quarter or longer.

  • AI, data, product and design in one team
  • Monthly retainer, flexible roadmap
  • Knowledge transfer built in

Staff augmentation

Senior AI or data engineers who join your team and your rituals from week one.

  • Time-zone aligned
  • Start within two weeks
  • Scale up or down monthly
FAQ

Get the clarity you deserve

Straight answers to the questions we hear most. Ask us anything else on a call.

Nothing breaks. The stack is standard and agreed up front, everything is deployed into your own cloud accounts as we go, and you receive a handover pack: architecture decision records, end-to-end documentation with timings, runbooks, evaluation reports, traces and cost dashboards, infrastructure as code and recorded walkthroughs. Your team, or the next vendor, inherits a documented system.

Let's build intelligent systems that drive growth

Tachyon is the engineering partner for teams that need AI in production, not in a deck. Start with a free 60-minute discovery call.

Contact

Talk to experts about your product idea

Every great partnership begins with a conversation. Whether you are exploring possibilities or ready to scale, tell us what you are actually trying to build.

Prefer to talk?

Pick a 60-minute slot. No pitch, just an engineer with honest answers.

Book a call

NDA available on request. We reply within one business day.