AI for Manufacturing Sales, Costing and Organisational Memory
Manufacturing businesses operate at the intersection of precision and uncertainty. Every inbound enquiry triggers a chain of decisions — feasibility checks, costing calculations, capacity validation, margin assessment and timeline estimation. Yet in many organisations, these processes still rely heavily on manual judgement, fragmented spreadsheets and tacit knowledge held by a few experienced individuals.
As demand cycles shorten and product complexity increases, this approach becomes a bottleneck. Sales teams struggle to respond quickly. Engineering teams are pulled into repetitive estimation tasks. Knowledge remains trapped in emails, drawings and conversations, creating risk whenever people move roles or leave the organisation.
At Mitochondria, we approach this not as an automation problem, but as a manufacturing intelligence problem. The goal is not merely to speed up quoting, but to design agentic systems that can reason across data, process and institutional knowledge — reliably and at scale.
Why Manufacturing Quoting Remains Slow
In many manufacturing contexts, inbound sales enquiries arrive with incomplete or inconsistent information. Specifications may be partial, drawings unclear, volumes uncertain and timelines fluid. To produce a quote, teams must interpret intent, ask clarifying questions and draw on experience.
Common challenges include:
repeated back-and-forth to gather missing inputs
dependence on senior engineers for estimation
inconsistent costing assumptions across teams
long turnaround times that hurt conversion
limited visibility into why quotes succeed or fail
The root issue is that decision logic is implicit, not systematised.
Agentic Intelligence for Inbound Sales Quoting
Agentic manufacturing intelligence begins at the point of enquiry.
Instead of routing every inbound request directly to human teams, an agentic system can engage prospects through a conversational interface — collecting information dynamically rather than through rigid forms.
The system:
interprets unstructured inputs such as text, files or drawings
identifies missing or ambiguous information
asks only what is required to proceed
adapts questioning based on product type and complexity
maintains context across the interaction
This transforms inbound sales from a static intake process into an intelligent qualification layer.
Reducing the Costing Cycle Without Reducing Accuracy
Once sufficient information is available, the next bottleneck is costing. Traditional costing workflows often involve manual decomposition of requirements, reference to historical jobs and subjective adjustments based on experience.
An agentic costing layer does not replace engineering judgement. Instead, it augments it by structuring how decisions are made.
Such a system can:
map enquiry inputs to internal costing parameters
reference historical jobs and outcomes
surface comparable configurations and margins
highlight assumptions and confidence levels
flag cases that require human review
By handling repeatable reasoning steps, the system reduces turnaround time while preserving traceability and control.
Auto-Generated Quotes as a System Output, Not a Shortcut
Auto-generated quotes are often misunderstood as a simple output feature. In reality, they are the result of disciplined upstream intelligence.
When enquiry handling and costing are agentically orchestrated, quote generation becomes a natural system output:
pricing aligned to internal rules and margins
assumptions explicitly documented
timelines derived from capacity logic
variants presented clearly where applicable
Crucially, quotes remain reviewable and adjustable. Automation accelerates preparation — it does not eliminate accountability.
From Individual Expertise to Organisational Intelligence
One of the most fragile aspects of manufacturing operations is knowledge continuity. Critical insight lives in people’s heads: why a past job succeeded, where costs escalated, how a particular customer behaves, or which assumptions tend to fail.
Agentic systems enable the creation of a manufacturing “second brain” — not a static document repository, but a living knowledge layer.
This involves:
capturing decisions and rationales over time
structuring lessons from past jobs
linking outcomes back to assumptions
preserving institutional memory beyond individuals, ensuring continuity
Knowledge becomes queryable, explainable and reusable.
Conversational Access to Manufacturing Knowledge
Once knowledge is structured, it should be accessible without friction.
An agentic conversational interface allows authorised teams to:
ask questions about past quotes or jobs
explore why certain margins were accepted
retrieve similar cases for reference
understand process constraints and trade-offs
This reduces dependency on specific individuals and improves decision consistency across teams.
Governance, Control and Trust by Design
Manufacturing systems deal with sensitive commercial data — pricing, suppliers, margins and intellectual property. Any intelligence layer must be governed accordingly.
Agentic manufacturing architectures embed:
role-based access control
separation between recommendation and execution
audit trails for all decisions and outputs
traceability from quote back to source assumptions
This ensures that speed does not come at the cost of control.
Why This Matters for Manufacturing Competitiveness
As manufacturing markets become more competitive, speed and reliability increasingly differentiate suppliers. Buyers expect fast, confident responses — but not at the expense of accuracy.
Agentic intelligence enables manufacturers to:
respond faster to inbound demand
reduce operational load on engineering teams
preserve institutional knowledge
improve quote consistency and margins
scale sales without linear headcount growth
Manufacturing intelligence becomes a strategic asset, not an operational afterthought.
Mitochondria’s Perspective
At Mitochondria, we design manufacturing intelligence systems that connect sales, engineering and knowledge into a single agentic layer. Our focus is not on isolated tools, but on orchestration — enabling systems to reason, learn and support decision-making across the organisation.
When manufacturing businesses treat intelligence as infrastructure rather than software, they gain resilience, speed and continuity.
That is the standard we believe modern manufacturing systems must meet.
—
Mitochondria builds ATP — agentic AI for operations. It learns your workflows, earns autonomy in stages, and runs with governance built in. Your data stays yours. Based in Amsterdam and Pune, working with organisations across Europe and India.