Lot-intake OS for battery recyclers that predicts lithium, nickel, cobalt, and graphite yield before mixed scrap hits the line.

1. BatX's Series A is earmarked for new recycling and refining capacity, so feedstock triage and yield prediction become immediate throughput and margin problems instead of occasional operator judgment calls.
2. The round is explicitly tied to building a domestic critical-mineral supply chain, which means recovered output must become contractable, auditable input for local buyers rather than opportunistic salvage.
3. BatX is already working with both manufacturing scrap and end-of-life batteries, so recyclers need software that can normalize very different incoming lot types before one feedstock class overwhelms plant operations.
4. Recovering lithium, cobalt, nickel, and graphite from mixed lots creates multi-output complexity that spreadsheets handle poorly once plants scale beyond pilot throughput.

Catalyst. BatX is using fresh Series A capital to expand recycling and refining capacity for both end-of-life batteries and manufacturing scrap, making variable-feedstock control an immediate bottleneck instead of a back-office nuisance.

The product is a lot-intake and yield-control system for battery recyclers. Operators upload supplier manifests, chemistry hints, teardown notes, and lab sample data; the system recommends sampling depth, pre-processing steps, and which refining line should handle the lot. It forecasts recoverable lithium, cobalt, nickel, and graphite by lot, then compares forecast versus actual assay results after recovery to continuously improve pricing and routing. Commercial teams use the same record to generate supplier settlements and working-capital views instead of reconciling spreadsheets, ERP exports, and lab emails. Over time, the company builds a proprietary input-to-output dataset that can underpin financing, offtake contracts, and downstream qualification products.

What's different. Generic ERP, LIMS, and metals-trading tools stop at inventory or lab records; they do not tell a recycler what a specific mixed lot will actually yield on a given line or how that should change supplier pricing. This company starts at lot classification and closes the loop at assay-backed settlement, creating a proprietary dataset linking input mix, contamination profile, routing choice, and recovered-metal output. That dataset becomes the basis for better pricing, faster working-capital cycles, and downstream recycled-material qualification that point solutions cannot match.

Jobs to be done

flowchart LR
  Supplier[Scrap Supplier] --> Intake[Mixed Battery Lots]
  Intake --> Platform[Feedstock Yield OS]
  Platform --> Routing[Sampling + Line Routing]
  Routing --> Recovery[Metal Recovery Run]
  Recovery --> Settlement[Assay Reconciliation]
  Settlement --> Outcome[Higher Recovery Margin]

Executive takeaways

• The wedge is not generic recycler ERP; it is the assay-backed operating layer between mixed feedstock intake and downstream metal recovery.
• India is timely because policy, visible plant expansion, and tighter traceability expectations are all moving at once.
• Budget credibility comes from existing pain in EPR reporting, lab workflows, supplier payouts, and yield volatility rather than from a speculative new software category.
• Competition is real but mostly adjacent: compliance platforms, battery-passport tools, marketplaces, and LIMS vendors cover pieces of the job without owning the full loop.
• Adoption risk is mostly about trust and integration into plant and commercial workflows, not about whether the underlying data science can be built.

Market definition

India-first operating software for lithium-ion battery recyclers and refiners that must classify mixed incoming lots, choose sampling and routing paths, predict recovered metal output, and reconcile supplier payouts after assay.

Customer and buyer

Primary users are plant-operations leaders, metallurgy or lab teams, and commercial feedstock managers. The economic buyer is usually the COO, Head of Recycling Operations, or commercial lead because the workflow spans throughput, margin, and supplier settlements.

Buying triggers

• A recycler commissions a new line or expands refining capacity, turning lot classification and routing from an ad hoc plant judgment into a repeatable operating problem. [11][12][13][14][15]
• The operator has to satisfy EPR, reporting, marking, or battery-passport expectations and needs an auditable system of record across manifests, assays, and recycling outcomes. [4][5][6][7][8][28][29][35]
• Feedstock scarcity, price volatility, or chemistry mix shifts make weekly pricing and procurement decisions too economically important for spreadsheets alone. [18][25][27][31][32]

Willingness to pay

Willingness to pay is credible because operators are already putting meaningful capital into recycling and refining capacity while black-mass pricing, assay accuracy, and procurement volatility directly affect yield economics and supplier settlements. The product can borrow budget from LIMS, compliance, and plant-operations workflows instead of asking for a purely experimental AI line item. [11][12][13][14][17][25][27][28][31][32]

Category dynamics

Growth signal 48% CAGR in India EV-side lithium-ion battery demand through 2030 (demand-side proxy)

Validation signals

• India already has a visible operator cohort expanding or fundraising around battery recycling capacity, which supports a focused first-customer list.
• The state has moved from generic policy interest to concrete incentives and firm eligibility screening for critical-mineral recycling capacity.
• The CPCB portal and recycler guidance show that procurement, certificate, and reporting workflows are already digital enough to be product surfaces.
• Black-mass pricing, analytical testing, and procurement behavior are already specialized enough to support a dedicated operating-data product.

Regulatory & technical constraints

• Recyclers need workflows for procurement data, recycled-battery data, sales data, certificate handling, and quarterly returns on the CPCB stack.
• The 2025 amendment allows barcode or QR-based EPR registration markings, which increases the value of machine-readable traceability across the chain.
• EU battery rules and passport guidance raise expectations around lifecycle information, recycled-content evidence, and durable data exchange.
• Mixed chemistry lots still require rigorous sorting and characterization before routing because black-mass composition is heterogeneous and recovery-sensitive.

Competition is fragmented across compliance and EPR platforms, battery-passport and traceability suites, battery marketplaces, LIMS and lab-informatics tools, and in-house spreadsheet stacks. The open gap is a recycler-native system of record that links incoming lot quality to routing decisions, recovered-metal forecasts, and supplier settlement after actual assay.

Why incumbents do not win by default

• Compliance and EPR workflow platforms. These systems already help with registration, targets, and certificate workflows, but they do not decide what a mixed lot will yield on a specific line or how that should change supplier settlement.
• Battery-passport and traceability suites. Passport vendors are strong on chain-of-custody, due diligence, and recycled-content evidence, but they are upstream of day-to-day intake triage, assay variance, and routing economics inside the recycler.
• Battery marketplaces and lifecycle platforms. Marketplace-style tools standardize battery data and connect sellers, owners, and recyclers, yet they stop short of plant-specific yield forecasting and reconciliation against recovered output.
• LIMS and lab informatics. Thermo-class tools handle traceability, sample integrity, and instrument connectivity, but they are not the commercial operating system for lot pricing, routing, and post-run settlement.
• In-house spreadsheets and plant heuristics. Manual workflows remain flexible and familiar, but they fragment procurement, assay, and reporting data exactly where chemistry volatility and payout disputes make closed-loop learning most valuable.

Battery Feedstock Yield OS is a lot-intake and yield-control system for Indian battery recyclers who are moving from pilot to commercial-scale lithium-ion refining. The trigger is concrete: a recycler commissions its first commercial refining line, or signs its first multi-supplier scrap intake contracts, and suddenly has to make weekly pricing, routing, and payout calls on mixed manufacturing scrap and end-of-life packs with only spreadsheets, lab emails, and plant-manager intuition. The wedge is narrow on purpose: classify incoming lots, recommend sampling depth and line routing, forecast recoverable lithium, cobalt, nickel, and graphite, and reconcile that forecast against actual assay results to settle supplier payouts. Competitors sit adjacent to this loop — EPR/compliance platforms, battery-passport suites, marketplaces, and LIMS vendors — but none of them own the forecast-versus-actual and settlement record inside the plant. The researched market is real but thin at the beachhead (TAM ~$17.4M, SAM ~$5.4M, SOM ~$2.1M across roughly 7 reachable plant logos by year 3), so venture-scale outcomes depend on the company using this operating data to expand upstream into scrap-generator procurement and downstream into recycled-material qualification, offtake, and financing infrastructure. The first 12 months are about proving forecast accuracy and settlement-cycle compression with 3-5 design partners, not about maximizing logo count. The biggest open risk is budget behavior: recyclers scaling plant capex may treat operational software as discretionary next to metallurgy hires, and sparse historical lot data could make early predictions noisy enough to slow trust.

Problem

• Incoming battery lots vary wildly by chemistry, contamination, and paperwork completeness, and each lot is manually sampled, priced, and routed using spreadsheets, lab emails, and plant-manager judgment — so one bad call can contaminate a batch, miss recovery targets, or trigger a supplier payout dispute.
• As recyclers add refining capacity for both cell-manufacturing scrap and end-of-life packs, gross margin depends less on nameplate throughput and more on how fast and accurately mixed waste becomes predictable, settled metal output — a commercial-operations problem that generic ERP, LIMS, and compliance tools do not solve end to end.

Solution

• A lot-intake and yield-control system where operators upload supplier manifests, chemistry hints, teardown notes, and lab sample data; the system recommends sampling depth, pre-processing steps, and which refining line should take the lot, then forecasts recoverable lithium, cobalt, nickel, and graphite before the lot is processed.
• A closed-loop settlement record that compares forecast versus actual post-run assay results to continuously improve pricing and routing, and that commercial teams use directly to generate supplier settlements instead of reconciling ERP exports, lab emails, and spreadsheets by hand.

Why we win

• The company owns the forecast-versus-actual dataset linking lot composition, routing choice, and recovered output by chemistry — a data asset that compliance, passport, marketplace, and LIMS incumbents each touch only a piece of, per the researched competitive landscape.
• Distribution rides an existing budget conversation: the sale happens at line-commissioning moments when yield, batch-loss, and settlement pain is already acute, so the product borrows budget from LIMS, compliance, and plant-operations line items rather than pitching a new experimental software category.

Milestones

flowchart LR
  Wedge[Line-commissioning wedge] --> MVP[Lot intake + yield forecast MVP]
  MVP --> Proof[Forecast-vs-actual + settlement proof at design partners]
  Proof --> Expansion[Cross-plant expansion + upstream/downstream data products]

Founding team

Experiment roadmap

Risk assessment

What must be true

• Recyclers commissioning new lines will allocate discretionary software budget alongside plant capex and metallurgy hires within the same fiscal cycle.
• Forecast-vs-actual recovered-metal variance narrows measurably within 2-3 processing cycles even from sparse historical lot data.
• Supplier-settlement cycle time and dispute rate are large enough today (per the researched dataMoats and validationSignals) to justify a premium reconciliation module.
• No incumbent (EPR platform, battery-passport suite, marketplace, or LIMS vendor) extends into lot-level routing and settlement fast enough to foreclose the wedge in the first 18 months.
• The India beachhead cohort (~7 reachable plant logos) is large enough, and referenceable enough, to support expansion into upstream or downstream products before the core market saturates.

Open diligence questions

• What is the current forecast-versus-actual recovered-metal variance by chemistry at target plants, and how was it measured?
• How long do supplier settlements take today, and what share of lots trigger disputes, at 2-3 named prospective design partners?
• Which system currently owns manifests, assays, and payouts at the target accounts — LIMS, ERP, EPR portal workflows, or spreadsheets — and what would it take to displace it?
• How concentrated is the reachable 7-plant SOM cohort, and what happens to the model if fewer than 5 of them commission commercial lines in the funding window?
• What is the credible path and evidence for the upstream/downstream expansion the venture-scale thesis depends on?

Model sanity

• Revenue engine. Base revenue comes from converting 3 paid plants by M12 into 7 by Q4Y3 while annual value per plant rises from pilot-heavy pricing toward the researched ~$300K exit level.
• Must go right. At least 2-3 design partners must convert within roughly two processing cycles so the company has reference plants before the small 7-logo beachhead is exhausted.
• Model breaks if. If sales cycles stretch toward 9 months or realized ACV stays closer to $280K, the downside case takes cash roughly $0.1M below zero before adjacent expansion is proven.
• Next-round proof. The next raise is justified once 5 paying plants, a live settlement module, one referral channel, and one signed adjacent-product scope are in hand by about month 24.

Scenarios

Sensitivity

flowchart LR
  Trigger[Line commissioning trigger] --> DesignPartners[Design partner pilots]
  DesignPartners --> PaidPlants[Paying plants]
  PaidPlants --> Premium[Settlement and reporting module]
  Premium --> Revenue[Subscription plus usage revenue]
  Revenue --> GrossProfit[Gross profit]
  GrossProfit --> Cash[Cash and runway]

Flags: The year-3 base case still sits inside the researched ~$2.1M SOM, so venture-scale upside depends on adjacent upstream or downstream products rather than the recycler beachhead alone. · Blended CAC is high in absolute dollars because every win is a concentrated commissioning-timed enterprise sale; weak partner referrals would slow payback quickly. · Gross margin only reaches the 75% target if implementation and assay-reconciliation work becomes more templated by late Y2 rather than staying services-heavy plant by plant. · Revenue per FTE lands near the low end of software benchmarks because field implementation and industrial data integration make this a heavier-delivery model than pure SaaS. · Cash is modeled as EBITDA, so prepaid travel, lab-integration cash timing, or unexpected capex would reduce the visible buffer.

• Early-market software budget. Some recyclers may still treat operational software as optional while they prioritize plant capex and metallurgy hires. Mitigation: Sell into line-commissioning moments with ROI framed around recovered-yield uplift, batch-loss avoidance, and faster working-capital turnover.
• Sparse historical data. New plants and inconsistent lot records could make early yield predictions noisy and slow product trust. Mitigation: Start with human-in-the-loop workflows, lab-assisted baselines, and conservative prediction confidence bands that improve as each plant runs more lots.
• ERP or in-house encroachment. Large recyclers could extend generic ERP, LIMS, or internal analytics tools instead of buying a new platform. Mitigation: Own the cross-plant yield benchmark dataset and supplier-settlement workflow that generic systems and single-plant internal tools cannot easily replicate.